Security Requirements Analysis Report

Comprehensive Security Analysis with Interactive Dashboard

Author

Security Requirements System v2.0

Published

November 19, 2025

Generated: 2025-11-19 19:04:00 Report Version: 2.0 - Comprehensive Security Analysis

1. Executive Summary

This section provides a high-level overview of the security requirements analysis, presenting key findings, validation results, and an interactive dashboard for stakeholders and decision-makers. The executive summary enables rapid comprehension of the security posture, critical risks, control coverage, and compliance status without requiring detailed technical knowledge.

1.1. Purpose and Scope

Purpose

This document presents a comprehensive security requirements analysis for the proposed application, systematically mapping high-level business requirements to specific, actionable security controls aligned with multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. The analysis provides a complete security requirements specification that guides secure system design, implementation, and verification.

Scope

This analysis encompasses all functional requirements provided, delivering comprehensive coverage across multiple security domains:

Requirements Analysis: Systematic decomposition and security-relevant extraction from business requirements
Stakeholder Analysis: Identification of stakeholders, trust boundaries, and security responsibilities
Threat Modeling: Systematic identification and assessment of security threats using STRIDE methodology
Security Control Mapping: Mapping requirements to multi-standard security controls (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed implementation guidance
Compliance Requirements: Identification of regulatory and legal compliance obligations
Architectural Security: Security architecture recommendations and design patterns
Implementation Planning: Prioritized, phased implementation roadmap
Verification Strategies: Testing and validation approaches for security controls

The analysis provides both strategic guidance for security planning and tactical details for implementation teams.

1.2. Key Findings

This section summarizes the most critical results from the security requirements analysis, providing executives and stakeholders with immediate insight into the security posture and validation status.

Analysis Metrics

Validation Score: 0.92/1.0
Validation Status: ✅ Passed
Analysis Iterations: 1
Requirements Analyzed: 25

Application Summary

An AI-powered code review and security analysis platform that integrates with Git repositories to automatically analyze commits and codebases (via LLM and static analysis), identify security vulnerabilities and secrets, score and explain risks, propose and optionally create fixes (PRs), support team collaboration and CI/CD integration, manage and serve models (inference and training) securely, and provide compliance reporting and audit logging while protecting sensitive source code, tokens, and configuration data.

The validation score reflects the quality and completeness of the security requirements across five dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. A score of 0.8 or higher indicates that the requirements are ready for implementation, while scores below this threshold may require refinement before proceeding.

1.3. Security Overview Dashboard

This interactive dashboard provides executive-level visualization of key security metrics and trends, enabling rapid assessment of the security posture through intuitive charts and data visualizations. The dashboard presents critical information across multiple dimensions: risk distribution, security control coverage, compliance status, implementation progress, and data quality metrics. For optimal viewing experience, render this document with Quarto to enable interactive chart functionality, allowing stakeholders to explore data dynamically and drill down into specific areas of interest.

Figure 1: Risk heat map showing threat distribution by likelihood and impact (1-5 scale).

Top 5 Highest Risks:

THR-001 (Critical) - Edge/API Gateway - Category: Spoofing - Likelihood: 4 | Impact: 4 - Description: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsigned/weakly-signed tokens) allowing an attacker to impersonate users or service accounts and access repositories, create

THR-009 (Critical) - AI Analysis Engine - Category: Information Disclosure - Likelihood: 4 | Impact: 4 - Description: Sensitive source code or secrets are transmitted to or stored on external managed model hosts (SaaS model providers) where tenant separation, contractual protections, or inadvertent logging could lead

THR-012 (High) - AI Analysis Engine - Category: Denial of Service - Likelihood: 4 | Impact: 3 - Description: Excessive inference requests (automated or malicious) saturate model hosting or GPU capacity, causing analysis delays or denial of analysis service to other tenants.

THR-002 (High) - Edge/API Gateway - Category: Tampering - Likelihood: 3 | Impact: 4 - Description: Manipulation of inbound API requests or webhook payloads en route to backend (e.g., altering scan parameters, redirecting callbacks, or injecting malicious fields) to change scan behavior or escalate

THR-003 (High) - Edge/API Gateway - Category: Denial of Service - Likelihood: 3 | Impact: 4 - Description: API gateway or edge infrastructure overwhelmed by high request volume or crafted requests (flooding, slowloris, large payloads) causing availability loss for scanning and developer access.

Figure 2: Security control distribution by standard (OWASP, NIST, ISO 27001).

Figure 3: OWASP ASVS control distribution by verification category (V1-V14).

Figure 4: Security control priority distribution (Critical/High/Medium/Low).

Coverage Metrics:

Total Security Controls Mapped: 74
- OWASP ASVS: 26 controls
- NIST SP 800-53: 28 controls
- ISO 27001: 20 controls
Requirements with Security Control Mapping: 89.3% (25/28)
Average Controls per Requirement: 2.6
Critical Controls: 26 (35.1% of total)
Requirements with Verification: 100.0% (28/28)
Recommended ASVS Level: L2 (Standard)

Figure 5: Compliance status across all applicable frameworks (Red-Amber-Green rating). Shows regulatory compliance (GDPR, HIPAA, PCI-DSS, etc.) and security standards (OWASP ASVS, NIST SP 800-53, ISO 27001).

Compliance Summary:

⚠️ OWASP ASVS: In Progress (Next Audit: N/A)
⚠️ NIST SP 800-53: In Progress (Next Audit: N/A)
⚠️ ISO 27001: In Progress (Next Audit: N/A)

Figure 6: Projected implementation timeline by phase and week (based on priority-based planning).

Implementation Timeline (Projected):

Phase 1 (Critical/High): 100% projected completion (Weeks 1-8)
Phase 2 (Medium): 100% projected completion (Weeks 9-16)
Phase 3 (Low/Ongoing): Continuous improvement and monitoring

Note: Timeline is based on priority-based planning and assumes steady implementation progress.

Validation Metrics:

Overall Validation Score: ✅ 0.92/1.0

Dimension Scores:

✅ Completeness: 0.92
✅ Consistency: 0.98
✅ Correctness: 0.90
✅ Implementability: 0.86
✅ Alignment: 0.95

Figure 7: Data quality and coverage metrics.

Traceability Matrix:

Total Requirements: 28
Linked to Threats: 28 (100.0%)
Mapped to Security Controls: 25 (89.3%)
With Verification: 28 (100.0%)

Data Quality: ✅ Excellent

2. Requirements Understanding

This section presents a comprehensive analysis of the functional requirements, extracting security-relevant information and establishing the foundation for the security requirements specification. Understanding the functional requirements is essential for identifying security implications, data sensitivity, trust boundaries, and security-critical components. This analysis transforms business requirements into security-aware specifications that inform threat modeling, control selection, and compliance assessment.

2.1. High-Level Requirements Analysis

The following high-level functional requirements have been identified and analyzed for security implications:

Repository integration with GitHub, GitLab, and Bitbucket (OAuth + webhooks) and support for real-time commit monitoring
Multi-language source code analysis (Python, Java, JavaScript, Go, etc.)
LLM-based code review engine (inference API) with fine-tuned models and model versioning
Static code analysis and pattern recognition for security anti-patterns
Dependency scanning for known CVEs and supply-chain vulnerabilities
Secret detection in code and configuration files
AI-generated remediation suggestions including code snippets and contextual explanations
Automatic pull request creation and optional auto-apply fixes with approval workflows
Severity scoring, risk assessment, and contextual prioritization
Developer dashboards with personalized security metrics and trend analysis
Issue comment threads, collaboration features, and integration with Slack/Teams notifications
Integration with CI/CD pipelines for pre-merge and post-merge scanning
OWASP Top 10 and custom security policy enforcement with customizable rules
Audit logging and immutable records of findings, decisions, and remediation history
Exportable compliance reports and evidence packages
Model management: inference API endpoints, versioning, A/B testing, performance monitoring, and drift detection
Custom model training on organization codebases with privacy-preserving controls
Secure storage and handling of code snippets, tokens, keys, and analysis results (encryption, retention, redaction)
Vulnerability database management and mapping to CVE/NVD feeds
Caching of analysis results for performance with cache invalidation policies
Access control and RBAC for repo access, model access, and UI/API operations
Rate limiting, abuse prevention, and API authentication/authorization
Support for on-premises or isolated VPC/SaaS deployment models and data residency controls
Consent, data usage policies, and ability to opt-in/opt-out model training on customer code
Monitoring, alerting, and incident response for platform security (SIEM integration)

2.2. Detailed Requirements Breakdown

Req ID	Requirement	Business Category	Security Sensitivity	Data Classification
REQ-001	Repository integration with GitHub, GitLab, and Bi…	Repository Integration / Authentication	High	Confidential
REQ-002	Support multi-language parsing and analysis for Py…	Analysis Engine	Medium	Internal
REQ-003	Provide an LLM-based code review engine exposed vi…	Model Management / Inference	High	Confidential
REQ-004	Perform static code analysis (SAST) and pattern re…	Analysis Engine / Security Testing	High	Confidential
REQ-005	Dependency scanning that identifies libraries, ver…	Dependency Management / Vulnerability Management	High	Internal
REQ-006	Secret detection across code, config files, and co…	Secret Management / Data Protection	High	Restricted
REQ-007	Generate AI remediation suggestions with code snip…	Remediation / Developer Productivity	Medium	Confidential
REQ-008	Support automatic pull request creation with propo…	Automation / CI/CD Integration	High	Confidential
REQ-009	Provide severity scoring and risk assessment that …	Risk Management	Medium	Internal
REQ-010	Context-aware explanations for each finding, inclu…	Reporting / Developer Support	Medium	Internal
REQ-011	Developer dashboards displaying personalized secur…	Team Collaboration / Analytics	Medium	Internal
REQ-012	Support comment threads on identified issues, assi…	Collaboration / Workflow	Medium	Internal
REQ-013	Integrate with CI/CD pipelines (e.g., GitHub Actio…	CI/CD Integration	High	Confidential
REQ-014	Send configurable notifications for critical findi…	Notifications / Collaboration	Medium	Internal
REQ-015	Provide OWASP Top 10 checks and allow creation/enf…	Compliance / Policy Enforcement	Medium	Internal
REQ-016	Maintain immutable audit logs of all analyses, fin…	Audit / Compliance	High	Restricted
REQ-017	Exportable compliance reports and evidence package…	Reporting / Compliance	Medium	Internal
REQ-018	Model management features including model versioni…	Model Management / Governance	High	Internal
REQ-019	Enable custom model training on customers’ codebas…	Model Training / Data Privacy	High	Restricted
REQ-020	Model performance monitoring and drift detection w…	Model Monitoring / Quality	Medium	Internal
REQ-021	Secure processing and storage of code snippets, an…	Data Handling / Security	High	Restricted
REQ-022	Maintain and update a vulnerability database mappe…	Vulnerability Management / Threat Intelligence	Medium	Internal
REQ-023	Cache analysis results for performance with secure…	Performance / Data Management	Medium	Confidential
REQ-024	Implement fine-grained access control and RBAC for…	Authentication / Authorization	High	Restricted
REQ-025	API gateway protections: API key management, OAuth…	API Security / Operational Security	High	Internal
REQ-026	Support deployment options: SaaS with VPC peering,…	Deployment / Infrastructure	High	Confidential
REQ-027	Data usage and consent controls including the abil…	Privacy / Data Governance	High	Restricted
REQ-028	Integrate platform monitoring with SIEM/SOAR for s…	Monitoring / Incident Response	High	Internal

2.3. Security Context and Regulatory Obligations

Applicable regulations and frameworks include: GDPR (personal data processing and cross-border data transfers) and national data protection laws for EU customers; CCPA/CPRA for California residents; SOC 2 Type II controls and ISO 27001 for information security management; NIST SP 800-53 / NIST CSF for federal or enterprise customers; OWASP Application Security standards (including OWASP Top 10) for web and API security; CWE/CVE/NVD for vulnerability classification; export control restrictions for cryptographic technologies; and industry-specific regulations as applicable (e.g., HIPAA if platform processes healthcare-related code containing PHI, PCI-DSS if code or artifacts contain payment card data). Legal obligations around intellectual property, contractual non-disclosure agreements, and customer-specific data residency or sovereignty requirements must be observed. Where models are trained on customer data, intellectual property and confidentiality clauses govern permissible use. Privacy expectations necessitate data minimization, consent, and the ability to erase or export customer data on request.

2.4. Assumptions

Platform is primarily cloud-hosted but must offer private SaaS/VPC or on-premises deployment options for customers with stricter controls.
Customers will provide OAuth consent and necessary repository scopes for analysis and webhook registration.
Users have internet access and modern browsers for the UI; CI/CD systems can reach the platform API endpoints.
Customers will provide clear authority to analyze and (optionally) train models on their codebases; legal contracts will define usage rights.
Encryption key management will be possible either via platform-managed KMS or customer-managed keys (BYOK) for enterprise customers.
Customers will classify their own data sensitivity; platform will annotate but not automatically override customer classification.
Real-time analysis is expected for commits via webhooks, while deeper analysis of full repositories can be scheduled asynchronously.
Third-party services (e.g., Slack, Teams, model hosting providers) used by the platform will meet security integration requirements (e.g., support for secure webhooks, tokens).

2.5. Constraints

Must support multiple Git providers (GitHub/GitLab/Bitbucket) and their differing OAuth/webhook APIs and permission models.
Must respect data residency and sovereignty constraints; some customers will require on-prem or single-tenant deployments.
Performance constraints: inference latency and scalability for LLMs are bounded by available compute and cost; caching and async workflows needed to meet SLAs.
Legal/contractual constraints: cannot use customer code for global model training without explicit opt-in and must provide deletion/export mechanisms.
Operational constraints: must maintain timely CVE feed updates and vulnerability intelligence; third-party feed outages must be handled gracefully.
Security constraints: all sensitive data must be encrypted in transit and at rest; support BYOK for enterprise customers and hardened secrets management.
Integration constraints: must integrate with diverse CI/CD tools and enterprise SSO systems (SAML/OIDC) with varying configuration complexity.
Model governance constraints: A/B testing and model rollouts must avoid exposing customer code to unapproved model variants and must track provenance and drift.
Availability constraints: platform must provide SLAs for availability and define maintenance windows, with options for higher availability in enterprise plans.
Compliance constraints: audit log retention periods, reporting formats, and evidence packages must satisfy multiple regulatory regimes and be configurable.

3. Stakeholder Analysis

This section identifies and analyzes all stakeholders involved in or affected by the system, including users, administrators, external partners, and regulatory bodies. Stakeholder analysis establishes trust boundaries, defines security responsibilities, and identifies potential security concerns from different stakeholder perspectives. Understanding stakeholder relationships and trust boundaries is critical for designing appropriate access controls, authentication mechanisms, and data protection measures.

3.1. Identified Stakeholders and User Personas

Role	Privilege Level	Trust Level	Key Security Concerns
Developer	User	Trusted	Unauthorized access to code repositories, exposure of sensitive data, insecure code practices.
Security Analyst	User	Trusted	Misidentification of vulnerabilities, reliance on faulty AI suggestions, data leakage during analysis.
System Administrator	Admin	Trusted	Privilege escalation by insiders, account compromise leading to system-wide access, misconfiguration of access controls.
Repository Owner	User	Trusted	Unauthorized access to repositories, exposure of sensitive tokens and keys, code integrity issues.
CI/CD Pipeline Service Account	Service Account	Partially Trusted	Insecure API integration, unintended code execution leading to vulnerabilities, exposure of build secrets.
External Integrator (Git Providers)	Service Account	Partially Trusted	Insecure OAuth integration, unauthorized access to code commits, potential data leakage.
AI Model Service Account	Service Account	Partially Trusted	Model exposure through insecure APIs, unauthorized access to training data, manipulation of inference results.
Compliance Auditor	User	Partially Trusted	Incomplete audit trails, unauthorized access to security findings, inability to verify compliance.
Notification System (Slack/Teams)	Service Account	Untrusted	Potential for unauthorized notifications revealing sensitive information, lack of encryption during data transfer.
Vulnerability Database Service	Service Account	Untrusted	Exposure of CVE data leading to exploitation, unauthorized access to vulnerability findings.

3.2. Trust Model

Trust boundaries are established at the user interface, API gateway, backend services, and data storage levels. Security mechanisms enforcing these boundaries include OAuth-based authentication for secure repository access, role-based access control (RBAC) to ensure users can only access data and functionalities pertinent to their roles, and network segmentation to limit exposure to critical services. Developers have access to their own repositories and analysis results, while Security Analysts can access vulnerability findings for remediation. System Administrators have comprehensive management access, while service accounts like CI/CD integrations and AI Model accounts operate under strict RBAC policies to minimize exposure. The principle of least privilege is implemented by ensuring that users and service accounts are granted only the permissions necessary for their specific tasks, effectively reducing the risk of unauthorized access and privilege escalation across the system.

4. System Architecture Analysis

4.1. Architectural Overview

The platform is a cloud-first, optionally private/SaaS system composed of a secure edge/API gateway and frontend for developers and CI/CD, core application services that orchestrate analysis workflows, an AI-powered analysis engine (LLM inference + SAST + dependency/secret scanning), a model management plane for training/versioning/inference, and a data layer that securely stores code artifacts, vulnerability data, audit logs, caches, and models. External integrations include Git providers (OAuth/webhooks), CVE/NVD feeds, notification systems (Slack/Teams), CI/CD pipelines, SSO providers, managed model hosts/GPUs, and SIEM. Components communicate via authenticated HTTPS APIs and message queues for async processing; sensitive artifacts and keys are encrypted and access-controlled via RBAC and KMS/BYOK.

4.2. Architecture Diagram

4.3. Component Breakdown

Component	Responsibility	Security Criticality	External Dependencies
Edge/API Gateway	Handle ingress traffic, OAuth flows, JWT…	Critical	SSO SAML/OIDC providers, WAF / DDoS protection
Frontend Layer	Web SPA and dashboards for developers an…	Medium	Browser clients, API Gateway
Application Services	Core orchestration, business logic, work…	Critical	GitHub/GitLab/Bitbucket APIs, CI/CD systems (GitHub Actions, Jenkins)
AI Analysis Engine	Perform LLM-based code review, static an…	Critical	Managed model hosts / GPU providers, Local/on-prem model infra (for private deployments)
Model Management	Host inference APIs, version models, man…	High	GPU/Model hosting providers, Model training pipelines
Data Storage & Persistence	Securely store code artifacts, vulnerabi…	Critical	KMS / BYOK providers, Immutable storage providers (WORM/append-only)
External Integrations	Connectors to third-party systems: Git p…	High	GitHub GitLab Bitbucket APIs, NVD/CVE feeds

4.4. Data Flow Analysis

Developers or CI/CD send commits/webhook events to the Edge/API Gateway which authenticates requests and forwards them to Orchestration. Orchestration fetches code via repository APIs (OAuth-scoped), stores encrypted artifacts, and enqueues analysis tasks. The AI Analysis Engine runs LLM inference and SAST, consults the Vulnerability DB, and writes findings to Audit Logs and Data Storage; remediation suggestions are generated and optionally open PRs back to the Git provider. Model Management serves inference endpoints and may receive opt-in training data in isolated environments. Results are cached for performance; dashboards and notifications surface findings. Sensitive data (code, tokens, secrets) are encrypted at rest and in transit; audit logs and exports are produced on demand.

4.5. Attack Surface Analysis

Primary entry points: API Gateway endpoints (inference APIs, REST APIs), OAuth webhook endpoints from Git providers, and the Web UI. These are high-risk and require strong authn/authz, rate limiting, WAF, and webhook signature validation. Integration points with Git providers and CI/CD are exposed via OAuth tokens and webhooks; compromised tokens can expose code—mitigate with least-privilege scopes, token rotation, and monitoring. Model inference endpoints are sensitive (risk of data exfiltration and prompt injection) and should enforce input sanitization, access controls, rate limits, and logging; A/B testing and training endpoints add risk for model leakage and must be isolated and opt-in. Data storage endpoints (artifact stores, caches, vulnerability DB, audit logs) represent a critical defensive boundary—protect with encryption, KMS, and strict IAM. Notification integrations (Slack/Teams) risk accidental exposure of secrets; notifications must redact code and include role-based visibility. Administrative interfaces and model management UIs need MFA and SSO enforcement. Overall risk priorities: OAuth/webhook integrations (High), Inference APIs and model training surfaces (High), Data stores and audit logs (Critical), Web UI and API Gateway (High), External feeds and notifications (Medium). Continuous monitoring, SIEM integration, automated anomaly detection, and incident response play a central role in reducing residual risk.

5. Threat Modeling

This section presents a comprehensive threat analysis of the system architecture and functional requirements. Threat modeling systematically identifies potential security vulnerabilities and attack vectors, enabling proactive risk mitigation through the application of appropriate security controls.

5.1. Threat Modeling Methodology

This analysis employs the STRIDE threat modeling methodology, a systematic framework developed by Microsoft for identifying security threats across six categories:

Spoofing Identity: Threats involving impersonation of users or systems
Tampering with Data: Threats involving unauthorized modification of data or system components
Repudiation: Threats where users deny performing actions (lack of non-repudiation)
Information Disclosure: Threats involving unauthorized access to sensitive information
Denial of Service: Threats causing disruption or unavailability of system services
Elevation of Privilege: Threats allowing unauthorized access to privileged functions

For each identified threat, the analysis evaluates likelihood (attack complexity and exposure) and impact (potential damage to confidentiality, integrity, or availability) to determine overall risk level. The methodology ensures comprehensive coverage of security concerns across all system components and interfaces.

5.2. Threat Analysis and Risk Assessment

5.2.1. Threat Overview

The following table provides a quick reference of all identified threats. Detailed analysis including descriptions, mitigation strategies, and residual risk assessment (where available) is provided in the section below.

Threat ID	Component	Category	Risk Level	Likelihood	Impact
THR-001	Edge/API Gateway	Spoofing	Critical	High	High
THR-009	AI Analysis Engine	Information Disclosure	Critical	High	High
THR-002	Edge/API Gateway	Tampering	High	Medium	High
THR-003	Edge/API Gateway	Denial of Service	High	Medium	High
THR-004	Frontend Layer	Information Disclosure	High	Medium	High
THR-006	Application Services	Tampering	High	Medium	High
THR-008	Application Services	Information Disclosure	High	Medium	High
THR-010	AI Analysis Engine	Tampering	High	Medium	High
THR-011	AI Analysis Engine	Elevation of Privilege	High	Medium	High
THR-012	AI Analysis Engine	Denial of Service	High	High	Medium
THR-014	Secret Detection / Data Storage	Information Disclosure	High	Medium	High
THR-015	Model Management	Tampering	High	Medium	High
THR-016	Model Management	Information Disclosure	High	Medium	High
THR-017	Data Storage & Persistence	Information Disclosure	High	Medium	High
THR-018	Data Storage & Persistence	Tampering	High	Medium	High
THR-019	External Integrations	Spoofing	High	Medium	High
THR-020	External Integrations	Information Disclosure	High	Medium	High
THR-022	Repository Integration (OAuth)	Spoofing	High	Medium	High
THR-023	CI/CD Integrations	Elevation of Privilege	High	Medium	High
THR-026	Data Handling / Caching	Information Disclosure	High	Medium	High
THR-027	Application Services / APIs	Tampering	High	Medium	High
THR-028	Edge/API Gateway	Elevation of Privilege	High	Low	High
THR-005	Frontend Layer	Spoofing	Medium	Medium	Medium
THR-007	Application Services	Repudiation	Medium	Medium	Medium
THR-013	AI Analysis Engine / Vulnerability Feeds	Information Disclosure	Medium	Medium	Medium
THR-021	External Integrations	Denial of Service	Medium	Medium	Medium
THR-024	Compliance and Reporting	Repudiation	Medium	Low	Medium
THR-025	Team Collaboration	Information Disclosure	Medium	Medium	Medium

Total Threats Identified: 28

5.2.2. Detailed Threat Analysis

This section provides comprehensive analysis of each identified threat, including descriptions, mitigation strategies, and residual risk assessment (where controls have been evaluated). Threats are organized by risk level for prioritized review.

Critical Risk Threats

THR-001 - Edge/API Gateway

Category: Spoofing
Likelihood: High | Impact: High
Initial Risk Level: Critical
Description: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsigned/weakly-signed tokens) allowing an attacker to impersonate users or service accounts and access repositories, create scans, or view results.
Mitigation Strategy: Enforce strict OAuth flows and short-lived tokens, validate JWT signatures and claims (iss/aud/exp), use proof-of-possession or mTLS for high-priv tokens, rotate tokens frequently, require token binding for webhooks, monitor for suspicious token usage, rate-limit token usage, and implement device/session fingerprinting. Enforce scope minimization on OAuth clients.
Controls Applied: OAuth best practices, JWT signature validation, mTLS, Token rotation, WAF
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-009 - AI Analysis Engine

Category: Information Disclosure
Likelihood: High | Impact: High
Initial Risk Level: Critical
Description: Sensitive source code or secrets are transmitted to or stored on external managed model hosts (SaaS model providers) where tenant separation, contractual protections, or inadvertent logging could lead to leakage of proprietary code or credentials.
Mitigation Strategy: Offer private/on-prem model deployment options, require E2E encryption (client to model), ensure model hosts provide data isolation contracts, avoid logging raw code snippets at the model host, minimize data sent to external providers (use extracted features), use BYOK and VPC peering for private deployments, and provide explicit opt-in for sending full code to managed hosts.
Controls Applied: Private model deployments, E2E encryption, BYOK, Contractual data protections
Control Effectiveness: Medium
Residual Risk Level: High
Status: ⚠️ Requires Review

High Risk Threats

THR-002 - Edge/API Gateway

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Manipulation of inbound API requests or webhook payloads en route to backend (e.g., altering scan parameters, redirecting callbacks, or injecting malicious fields) to change scan behavior or escalate access.
Mitigation Strategy: Validate webhook signatures (HMAC), verify sender IPs where feasible, use mutual authentication (mTLS) for integrations, perform strict input validation and schema checks at gateway, sign and/or timestamp important parameters, and record an immutable request audit trail.
Controls Applied: Webhook signing (HMAC), Input validation, mTLS, WAF
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-003 - Edge/API Gateway

Category: Denial of Service
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: API gateway or edge infrastructure overwhelmed by high request volume or crafted requests (flooding, slowloris, large payloads) causing availability loss for scanning and developer access.
Mitigation Strategy: Implement rate limiting, request throttling, bot detection, WAF and DDoS protection, circuit breakers, autoscaling with quotas, request size limits, and progressive backoff for heavy analysis requests. Use API keys and quotas per tenant.
Controls Applied: WAF, Rate limiting, DDoS protection, Quotas
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-004 - Frontend Layer

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, findings or tokens to other users or remote hosts; stored XSS in comment threads revealing sensitive diffs.
Mitigation Strategy: Apply strict output encoding/escaping, implement a strong Content Security Policy (CSP), sanitize user inputs, adopt secure templating frameworks, use HttpOnly and Secure cookies, enforce same-site cookie attributes, and perform regular frontend security testing.
Controls Applied: CSP, Output encoding, Input sanitization
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-006 - Application Services

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Forged repository webhook events or API calls cause the application to perform unauthorized actions such as merging PRs, applying fixes, or changing RBAC settings.
Mitigation Strategy: Verify webhook signatures, use per-integration secrets, enforce least privilege for integration accounts, require branch protection and signed commits for automatic merges, and add manual approvals for high-impact automated fixes.
Controls Applied: Webhook signatures, Least privilege, Branch protection, Signed commits
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-008 - Application Services

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Broken access control leading to unauthorized users (or tenants) reading repository metadata, scan results, vulnerability details, or remediation suggestions.
Mitigation Strategy: Enforce RBAC/ABAC with least privilege, implement strong multi-tenant isolation, perform authorization checks server-side for each resource, adopt just-in-time access, and provide fine-grained data redaction/scoping.
Controls Applied: RBAC/ABAC, Multi-tenant isolation, Server-side authorization
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-010 - AI Analysis Engine

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Poisoning of training data or prompt injection via crafted code causing models to generate insecure or malicious remediation suggestions (e.g., suggesting credential exfiltration or insecure configurations).
Mitigation Strategy: Validate and sanitize training data, maintain provenance of training artifacts, isolate customer training sets, perform adversarial testing and red-team model evaluation, implement prompt and output filtering, and require deterministic SAST cross-checks for suggested fixes.
Controls Applied: Training data provenance, Adversarial testing, Output filters, SAST cross-validation
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-011 - AI Analysis Engine

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: LLM-generated remediation automatically introduces code that elevates privileges (e.g., creating backdoor admin endpoints or insecurely setting permissions) and is merged without adequate review.
Mitigation Strategy: Require human review or automated safety checks for high-risk code changes, implement automated SAST/IAST validation of suggested patches, prevent automatic merges for critical severity changes, and enforce branch protections and signed approvals for elevated-permission changes.
Controls Applied: Human-in-the-loop, SAST validation, Branch protection
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-012 - AI Analysis Engine

Category: Denial of Service
Likelihood: High | Impact: Medium
Initial Risk Level: High
Description: Excessive inference requests (automated or malicious) saturate model hosting or GPU capacity, causing analysis delays or denial of analysis service to other tenants.
Mitigation Strategy: Implement per-tenant quotas, rate-limiting, queueing/backpressure, autoscaling of inference clusters, graceful degradation to lightweight scanners, and cost-control safeguards. Monitor usage and detect anomalous patterns.
Controls Applied: Quotas, Rate limiting, Autoscaling, Usage monitoring
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-014 - Secret Detection / Data Storage

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Secrets (API keys, tokens, private keys) stored in persistent storage, caches, backups, logs, or remediation history in plaintext or weakly encrypted form leading to leak and unauthorized access to third-party systems.
Mitigation Strategy: Encrypt secrets at rest using KMS/BYOK, use envelope encryption, redact secrets from UI/logs, avoid storing full secrets (store hashed/hashed pointers), rotate secrets on detection, implement strict access controls and auditing for key material.
Controls Applied: KMS/BYOK, Envelope encryption, Secret redaction, Key rotation
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-015 - Model Management

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Unauthorized modification or replacement of model binaries or metadata (model poisoning or substitution) causing compromised inference results across tenants.
Mitigation Strategy: Secure model registries with RBAC, sign model artifacts, use integrity checks (hashes/signatures), store models in hardened immutable storage, require approvals for model changes, and log/model change audit trails.
Controls Applied: Model signing, RBAC for registry, Immutable storage, Change approvals
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-016 - Model Management

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Leakage of model binaries or customer-specific training data/artifacts used for fine-tuning (e.g., code used in training accessible to other customers or leaked externally).
Mitigation Strategy: Isolate customer training environments (VPC/on-prem), encrypt training datasets and artifacts, use access controls and logging for training storage, avoid multi-tenant mixing of data during training, and provide contractual controls for managed hosts.
Controls Applied: Training environment isolation, Encrypted storage, RBAC, BYOK
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-017 - Data Storage & Persistence

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Public exposure of backups, snapshots, or misconfigured storage buckets containing code artifacts, vulnerability DB, or audit logs (e.g., publicly accessible S3 bucket).
Mitigation Strategy: Enforce least-privilege IAM for storage, block public access by default, encrypt backups with KMS/BYOK, perform automated scans for open storage, enforce MFA and approval for snapshot exports, and implement immutable retention where required.
Controls Applied: Block public access, KMS encryption, IAM least privilege, Storage scanning
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-018 - Data Storage & Persistence

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Unauthorized modification or deletion of audit logs, remediation history, or vulnerability records to erase evidence or alter findings.
Mitigation Strategy: Store critical logs in WORM/append-only storage, forward copies to a hardened SIEM, apply strict IAM and separation of duties for log access, cryptographically sign logs, and enable integrity verification.
Controls Applied: WORM storage, SIEM forwarding, Signed logs, Separation of duties
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-019 - External Integrations

Category: Spoofing
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed events or uses stolen integration credentials to manipulate the platform (trigger scans, inject malicious webhooks).
Mitigation Strategy: Enforce webhook signature verification, use short-lived integration credentials, require mutual TLS or IP allowlisting where possible, restrict delegated permissions, and monitor integration behavior for anomalies.
Controls Applied: Webhook signatures, Short-lived credentials, IP allowlisting, Integration monitoring
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-020 - External Integrations

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Integration tokens, API keys, or credentials logged in plain-text (application logs, access logs, stack traces) or included in notifications (Slack/Teams) leading to compromise of third-party services.
Mitigation Strategy: Redact secrets from logs and notifications, store tokens encrypted (KMS), rotate integration tokens frequently, implement scoped tokens with minimal privileges, and apply strict logging policies to avoid sensitive data in outputs.
Controls Applied: Secret redaction, KMS for tokens, Scoped tokens, Token rotation
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-022 - Repository Integration (OAuth)

Category: Spoofing
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: OAuth application granted excessive scopes or abused consent to gain broad repository access; attacker leverages app to exfiltrate code or create/merge PRs.
Mitigation Strategy: Require least-privilege scopes for OAuth apps, implement granular scope review and just-in-time elevation for higher scopes, show clear consent screens, use fine-grained tokens, and implement periodic automatic token and scope reviews.
Controls Applied: Scoped OAuth tokens, Periodic scope review, Least privilege
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-023 - CI/CD Integrations

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Malicious pipeline step or compromised CI runner introduced via a PR allows execution of malicious code with elevated privileges (e.g., pushing to production or accessing secrets), especially when automatic merges are permitted.
Mitigation Strategy: Enforce pipeline protection (trusted runners), require CI job signatures or provenance, restrict secrets to ephemeral tokens injected only at runtime, require code reviews for pipeline changes, and avoid automatic merge-to-main without approvals.
Controls Applied: Trusted CI runners, Secret injection at runtime, Pipeline change reviews, Signed CI artifacts
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-026 - Data Handling / Caching

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Cache poisoning or tenant key collisions cause cached analysis results, diffs, or findings to be returned to the wrong tenant or user, exposing proprietary code or vulnerability data.
Mitigation Strategy: Enforce tenant-scoped cache keys, encrypt cache values where sensitive, set conservative TTLs for cached code/artifacts, isolate caches per tenant when feasible, and validate cache hit tenant context before returning results.
Controls Applied: Tenant-scoped cache keys, Cache encryption, TTL management
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-027 - Application Services / APIs

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Classic injection attacks (SQL injection, command injection) or unsafe deserialization in API endpoints exposing back-end data stores or enabling remote code execution within application services.
Mitigation Strategy: Use parameterized queries/ORMs, strict input validation and allow-lists, safe deserialization libraries, WAF protections, regular dependency vulnerability management, and secure coding practices plus SAST/DAST testing in CI.
Controls Applied: Parameterized queries, Input validation, WAF, SAST/DAST
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-028 - Edge/API Gateway

Category: Elevation of Privilege
Likelihood: Low | Impact: High
Initial Risk Level: High
Description: Misconfiguration in gateway RBAC or routing allows privilege escalation to administrative APIs (e.g., a normal user can access management endpoints or modify ACLs).
Mitigation Strategy: Harden gateway admin endpoints behind separate network controls and MFA, apply strict RBAC/ABAC for management APIs, separate admin plane from data plane, perform regular configuration audits, and use automated policy-as-code tests on deployments.
Controls Applied: Admin plane isolation, RBAC, MFA, Configuration audits
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

Medium Risk Threats

THR-005 - Frontend Layer

Category: Spoofing
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating PRs with suggested fixes) performed by authenticated users without their knowledge.
Mitigation Strategy: Enforce anti-CSRF tokens or same-site cookies for state-changing operations, require re-authentication/MFA for critical actions, validate Origin/Referer, and use strict CORS policies.
Controls Applied: CSRF tokens, SameSite cookies, CORS validation
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-007 - Application Services

Category: Repudiation
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who initiated a scan, who created/approved a PR), complicating incident response and compliance.
Mitigation Strategy: Generate immutable, tamper-evident audit logs with strong timestamps, forward logs to a hardened SIEM, implement write-once storage for critical logs, cryptographically sign important events, and implement log retention and access controls.
Controls Applied: Immutable logs (WORM), SIEM integration, Signed audit events
Control Effectiveness: Medium
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-013 - AI Analysis Engine / Vulnerability Feeds

Category: Information Disclosure
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability metadata causing false positives/negatives or incorrect remediation advice.
Mitigation Strategy: Use signed/verifiable CVE feeds, cross-validate vulnerabilities from multiple trusted sources, implement caching with integrity verification, and maintain an internal vulnerability DB with human review for high-impact items.
Controls Applied: Signed feeds, Feed integrity checks, Cross-validation
Control Effectiveness: Medium
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-021 - External Integrations

Category: Denial of Service
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Flooding of webhook endpoints or continuous integration triggers to consume resources (e.g., force repeated scanning or push heavy workloads) causing degraded service.
Mitigation Strategy: Implement webhook rate limiting, signature verification, debounce/aggregation for repeated events, and require challenge-response during initial webhook registration. Provide per-tenant quotas.
Controls Applied: Rate limiting, Webhook signatures, Event debouncing
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-024 - Compliance and Reporting

Category: Repudiation
Likelihood: Low | Impact: Medium
Initial Risk Level: Medium
Description: Tampering with compliance reports or trend analysis dashboards (intentional or accidental) to hide vulnerabilities, creating false compliance posture.
Mitigation Strategy: Cryptographically sign and timestamp exported reports, maintain immutable archived copies for audits, enforce access controls for report generation and modification, and monitor report generation and download events.
Controls Applied: Signed reports, Immutable archives, Access controls
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-025 - Team Collaboration

Category: Information Disclosure
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Comment threads or dashboards inadvertently exposing sensitive code, PII, or secrets to unauthorized team members or external collaborators.
Mitigation Strategy: Enforce RBAC for comment visibility, auto-redact detected secrets and sensitive code snippets in comments, allow configurable visibility (private/public), and provide data minimization by default.
Controls Applied: RBAC for comments, Auto-redaction, Visibility controls
Control Effectiveness: Medium
Residual Risk Level: Low
Status: ⚠️ Requires Review

Risk Reduction Summary:

Critical Risk Reduction: 2 threats reduced from Critical to lower levels
High Risk Reduction: 20 threats reduced from High to lower levels
Residual Risk Distribution: 1 threats remain at Critical/High level

5.3. Risk Summary

The highest-severity threats center around authentication/token spoofing, sensitive-data exposure to external model hosts, tampering with model/training artifacts, and broken access controls enabling unauthorized reads or actions. Critical entry points are the Edge/API Gateway (OAuth/JWT handling, webhooks), AI Analysis Engine/model management (external model hosts, training pipelines, model registry), and Data Storage (backups, caches, secret storage). Attack vectors include stolen/over-privileged OAuth tokens, forged webhooks, model prompt injection or poisoning, misconfigured storage exposing code/artifacts, and abuse of automated remediation (automatic PRs/merges). Overall posture is high-risk in areas where sensitive code and secrets intersect with external services and where automation can change code or permissions without enforced human oversight. Priority controls: (1) Harden authentication and token management at the gateway (short-lived tokens, mTLS, JWT validation); (2) enforce strict multi-tenant isolation and encryption for code artifacts and model training data (BYOK/KMS, private model hosting); (3) add verification and human-in-the-loop gates for automatic remediation and high-impact actions; (4) secure the model management pipeline (artifact signing, RBAC, integrity checks); (5) apply robust logging/audit immutability and SIEM monitoring; and (6) implement rate limiting, quotas and WAF/DDoS protections. Immediate attention should focus on token and webhook security, protections around model inference/training data, and preventing automatic application of untrusted fixes. Regular security testing (SAST/DAST, adversarial model testing), configuration audits, and a strong incident response plan are recommended to reduce residual risk.

6. Multi-Standard Security Requirements Mapping

This section maps each functional requirement to specific security controls from multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. This multi-standard approach provides comprehensive coverage across application-level, enterprise-level, and organizational-level security domains:

OWASP ASVS: Application-level security controls (code, APIs, authentication, session management)
NIST SP 800-53: Enterprise security controls (governance, risk management, incident response)
ISO 27001: Information security management controls (policies, procedures, organizational controls)

Requirements are prioritized based on risk assessment and compliance needs, with controls selected from the most appropriate standard(s) for each requirement type.

6.1. Recommended ASVS Compliance Level

Recommended Level: L3

Level 3: Advanced

Required for high-security applications with stringent protection requirements. Includes advanced security controls, detailed verification procedures, and enhanced threat resistance. Suitable for applications handling highly sensitive data (e.g., financial, healthcare, government) or operating in high-risk environments.

The recommendation considers factors such as:

Data sensitivity and classification levels
Regulatory and compliance requirements (GDPR, HIPAA, PCI-DSS, etc.)
Threat landscape and risk assessment from threat modeling
Business criticality and potential impact of security incidents

All security controls referenced in this document align with this recommended compliance level.

6.2. Requirements Mapping

This section maps each high-level requirement to specific security controls from multiple standards (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed descriptions, relevance explanations, and integration guidance. Controls are grouped by standard for clarity.

6.2.1. REQ-001: Repository integration with GitHub, GitLab, and Bitbucket (OAuth + webhooks) and support for real-time commit monitoring

OWASP ASVS Controls

ASVS 2.3.1

Requirement: Ensure secure single sign-on, federated authentication and OAuth integration following best practices; validate token handling and scopes.

Relevance: OAuth and SSO are core to integrating with external repository providers; this control ensures tokens and OAuth flows are implemented securely which directly applies to repository integration. It also highlights validating scopes to limit access from integrations and webhook handlers.

Integration Tips: Use proven OAuth libraries, validate and enforce minimal scopes, rotate and store tokens securely, and validate webhook payload signatures. Implement token scope reviews and periodic audits of authorized integrations.

Verification Method: Review OAuth implementation code/config; inspect scope requested during OAuth flows; verify webhook signature verification and token storage protections during pen test and code review.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AC-3

Requirement: The information system enforces approved authorizations for controlling access to the system and its resources in accordance with applicable access control policies.

Relevance: Repository access via OAuth/webhooks must enforce authorization policies so only permitted repositories and users can be accessed; AC-3 maps to enforcing those approvals.

Integration Tips: Enforce access control checks server-side for actions initiated by repo integrations; map repository identities to internal accounts and roles and validate permissions before performing actions.

Verification Method: Inspect access control logic; run tests that attempt unauthorized repo access; review logs confirming enforcement of authorization decisions.

Priority: Critical

ISO 27001:2022 Controls

A.9.4.2

Requirement: Where required by the access control policy, secure log-on procedures shall be implemented to confirm the identity of users.

Relevance: OAuth login flows and automated repo integrations require secure authentication and login procedures to confirm identity before granting access to repositories or enabling webhooks.

Integration Tips: Document and enforce secure log-on requirements for users connecting repositories; implement multi-factor authentication where appropriate and secure OAuth redirection endpoints.

Verification Method: Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens.

Priority: High

6.2.2. REQ-002: Multi-language source code analysis (Python, Java, JavaScript, Go, etc.)

OWASP ASVS Controls

ASVS 8.1.1

Requirement: Perform architecture and design level analysis and include language-specific security considerations for all supported languages.

Relevance: Multi-language analysis requires considering language-specific threats and patterns; this control mandates language-aware threat modeling and design that matches the requirement.

Integration Tips: Develop language-specific analysis rulesets and threat models; include language experts when defining detection heuristics and tailor scanners to idioms of each language.

Verification Method: Review threat models and rulesets for each supported language; test scanners against seeded test cases covering language-specific issues.

Level: L2 | Priority: High

ASVS 14.1.1

Requirement: Source code should be reviewed by automated tools and manually to detect flaws across supported languages.

Relevance: Specifies using both automated and manual reviews across languages to detect issues tools may miss, addressing multi-language analysis completeness.

Integration Tips: Combine static analysis with manual review workflows; ensure analysis rules cover language-specific anti-patterns and that findings are validated by security reviewers.

Verification Method: Audit sampled findings, review false-positive rates per language, and confirm manual review steps exist in process documentation.

Level: L2 | Priority: High

NIST SP 800-53 Controls

SA-11

Requirement: The organization requires developers to employ security testing tools and techniques appropriate to the technology and languages used.

Relevance: Mandates using appropriate security testing for each language, matching multi-language analysis requirement to use correct tooling and coverage.

Integration Tips: Integrate language-specific static analyzers and linters into the analysis pipeline and CI; validate tool outputs and tune rules per language.

Verification Method: Verify tool inventory and configurations include language-specific scanners; run comparative analysis with benchmark projects.

Priority: High

6.2.3. REQ-003: LLM-based code review engine (inference API) with fine-tuned models and model versioning

OWASP ASVS Controls

ASVS 12.1.1

Requirement: Ensure secure design and versioning for APIs and that API endpoints enforce authentication, authorization and logging.

Relevance: Inference APIs must implement secure API practices including authN/Z, versioning in endpoints, and logging to meet this control.

Integration Tips: Apply token-based auth, API keys with scoped permissions, request/response logging with redaction, and explicit versioning strategies for endpoints.

Verification Method: Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SA-15

Requirement: The organization requires that development processes and tools support secure design, testing, and versioning of components and models used in systems.

Relevance: Directly maps to managing model lifecycle, versioning, and secure development practices for an LLM-based inference API and fine-tuning processes.

Integration Tips: Implement model CI/CD with version control for model artifacts, access controls for training and inference endpoints, and documented secure development lifecycle for models.

Verification Method: Review model versioning logs, CI/CD pipelines for model deployment, and security gating on model promotion to production.

Priority: Critical

ISO 27001:2022 Controls

A.12.5.1

Requirement: Changes to organization assets and services should be controlled and managed including versioning and rollback procedures.

Relevance: Applies to model versioning and controlled deployment of inference endpoints with ability to roll back if a model introduces vulnerabilities or unsafe behavior.

Integration Tips: Use structured change management for model updates, require approvals, keep roll-back images, and maintain audit trails for model changes.

Verification Method: Inspect change tickets for model deployments, verify rollback procedures, and confirm version metadata is immutable and auditable.

Priority: High

6.2.4. REQ-004: Static code analysis and pattern recognition for security anti-patterns

OWASP ASVS Controls

ASVS 14.2.1

Requirement: Use automated static analysis tools to detect common security weaknesses and anti-patterns in source code.

Relevance: This control explicitly requires automated static analysis to find anti-patterns, matching the requirement.

Integration Tips: Deploy a static analysis pipeline with language-specific rules, tune detectors to reduce false positives, and integrate with developer workflows for remediation.

Verification Method: Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

RA-5

Requirement: The organization scans for vulnerabilities and identifies vulnerabilities in systems and applications using automated tools.

Relevance: Supports requirement to scan code for vulnerabilities and anti-patterns using automated tooling.

Integration Tips: Schedule regular scans, include scans in CI, and create processes to triage and remediate findings discovered by static analysis.

Verification Method: Verify scheduled scans run; check vulnerability tickets and remediation timelines; validate coverage of scanning tools.

Priority: High

6.2.5. REQ-005: Dependency scanning for known CVEs and supply-chain vulnerabilities

OWASP ASVS Controls

ASVS 14.4.1

Requirement: Verify and manage third-party components and dependencies; ensure known vulnerabilities are tracked and remediated.

Relevance: Specifies verifying third-party components and tracking vulnerabilities, matching dependency scanning needs.

Integration Tips: Automate dependency scanning in CI, maintain a component inventory, and apply policies for allowed components and remediation SLAs.

Verification Method: Audit dependency scan reports, confirm mapping to CVE/NVD, and verify remediation tickets exist for critical findings.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SA-12

Requirement: The organization manages supply chain risks and implements protections against supply chain vulnerabilities.

Relevance: Directly relevant: dependency scanning and managing third-party components is a supply chain protection activity.

Integration Tips: Ingest CVE/NVD feeds, maintain SBOMs, enforce policy gates for vulnerable dependencies and automate patch and update suggestions.

Verification Method: Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI.

Priority: Critical

ISO 27001:2022 Controls

A.15.1.1

Requirement: Organizations should identify and manage security risks associated with supplier relationships and third-party components.

Relevance: Dependency scanning is a control to manage supply chain risks and aligns with supplier relationship policies.

Integration Tips: Include third-party component checks in supplier assessments and contractual obligations; require notification of vulnerabilities from suppliers.

Verification Method: Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans.

Priority: High

6.2.6. REQ-006: Secret detection in code and configuration files

OWASP ASVS Controls

ASVS 14.3.1

Requirement: Credentials and secrets must not be hard-coded; repositories must be scanned to detect accidental leakage of secrets.

Relevance: Directly applicable: requires detection of secrets in repos and preventing hard-coded credentials.

Integration Tips: Integrate secret scanning in pre-commit hooks and CI, block commits with detected secrets, and provide secure secret storage alternatives (vaults).

Verification Method: Run secret scanning on historical repositories, validate detection of seeded secrets, and confirm blocking policy enforcement.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SC-12

Requirement: Cryptographic keys and secrets must be managed securely and protected from unauthorized disclosure.

Relevance: Ensures that secrets detected are not stored insecurely and that key management practices protect secrets found in code/configs.

Integration Tips: Route detected secrets into a secure key management lifecycle, rotate exposed credentials immediately, and require replacement workflows.

Verification Method: Inspect KM systems, verify rotation and revocation procedures were executed for exposed secrets, and review access controls to secrets.

Priority: Critical

ISO 27001:2022 Controls

A.10.1.1

Requirement: A policy on the use of cryptographic controls should be developed and implemented to protect the confidentiality, integrity and availability of information.

Relevance: Supports secret protection by ensuring cryptographic controls and policy guide handling of secrets discovered during scans.

Integration Tips: Define policies requiring use of vaults, encryption for stored secrets, and handling procedures for detected secrets in code repositories.

Verification Method: Review policy documents, confirm applied cryptographic controls, and inspect evidence of secret handling procedures.

Priority: High

6.2.7. REQ-007: AI-generated remediation suggestions including code snippets and contextual explanations

OWASP ASVS Controls

ASVS 12.1.1

Requirement: Ensure APIs delivering code suggestions enforce authentication, authorization, and input/output validation.

Relevance: APIs delivering remediation must be secured to avoid misuse and must validate both inputs (code contexts) and outputs (prevent leaking secrets).

Integration Tips: Enforce authN/Z on suggestion endpoints, sanitize outputs to avoid leaking sensitive data, and log suggestion generation events for audit.

Verification Method: Pen-test suggestion APIs, inspect redaction logic for outputs, and verify auth and logging configuration.

Level: L2 | Priority: High

NIST SP 800-53 Controls

PL-4

Requirement: Define acceptable use and rules for automated tools and AI, specifying responsibilities and guidance for generated content.

Relevance: AI-generated remediation must be governed by rules of behavior to ensure safe and appropriate usage and to manage responsibilities for suggested changes.

Integration Tips: Publish guidance for accepting AI suggestions, require human review for high-impact fixes, and document responsibilities for code changes based on AI outputs.

Verification Method: Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions.

Priority: High

ISO 27001:2022 Controls

A.14.2.3

Requirement: Protection of data used for testing and development including sanitized code samples and generated outputs.

Relevance: AI-generated snippets must be handled as potentially sensitive test/development data; this control ensures outputs are sanitized and handled per policy.

Integration Tips: Sanitize training/test data, strip secrets from generated snippets, and apply classification/handling rules to AI outputs before display or storage.

Verification Method: Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage.

Priority: High

6.2.8. REQ-008: Automatic pull request creation and optional auto-apply fixes with approval workflows

OWASP ASVS Controls

ASVS 14.1.1

Requirement: Source code changes should be subject to review and must be tracked by secure processes including merge/PR workflows.

Relevance: Specifies that code changes (including automated ones) must be tracked and reviewed, aligning with the PR and approval workflow requirement.

Integration Tips: Ensure automated PRs include evidence of the vulnerability, suggested changes, and require at least one human reviewer before merge for sensitive fixes.

Verification Method: Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs.

Level: L2 | Priority: High

NIST SP 800-53 Controls

CM-3

Requirement: Changes to system configuration and software must be controlled, including approval and authorization prior to implementation.

Relevance: Auto-apply fixes and PR creation must be subject to change control and approvals to avoid unauthorized or unsafe automatic changes.

Integration Tips: Implement approval gates, require signed commits for auto-applied changes, and log automated change proposals with audit trails.

Verification Method: Verify workflow enforces approvals, review automated PRs created and ensure a rollback path exists with audit logs.

Priority: Critical

ISO 27001:2022 Controls

A.12.1.2

Requirement: Changes to information processing facilities and systems should be managed following formal change control processes.

Relevance: Auto-apply fixes must adhere to formal change management to ensure integrity and accountability of deployments.

Integration Tips: Tie auto-PRs to change tickets, require approvals for auto-apply in production, and maintain records of applied changes and approvals.

Verification Method: Review change records for auto-applied fixes, confirm approval traces, and test rollback processes.

Priority: High

6.2.9. REQ-009: Severity scoring, risk assessment, and contextual prioritization

OWASP ASVS Controls

ASVS 14.4.1

Requirement: Vulnerabilities should be prioritized by severity and context to guide remediation efforts.

Relevance: Directly supports contextual prioritization and severity scoring of vulnerabilities identified by the platform.

Integration Tips: Map scanner findings to severity taxonomies, incorporate context like code exposure and exploitability, and surface prioritized lists to developers.

Verification Method: Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans.

Level: L2 | Priority: High

NIST SP 800-53 Controls

RA-3

Requirement: Conduct risk assessments to identify threats and vulnerabilities and determine risk levels for organizational operations and assets.

Relevance: Provides framework for scoring and assessing the risk of findings, which is necessary for prioritization and remediation planning.

Integration Tips: Implement contextual scoring models that consider asset criticality, exploitability, and business impact; align scoring with organizational risk tolerance.

Verification Method: Review risk assessment methodology, sample scored findings and ensure scoring matches defined criteria and business context.

Priority: Critical

ISO 27001:2022 Controls

A.8.2.1

Requirement: Information should be classified to support risk-based handling and prioritization.

Relevance: Classification of assets and codebases supports contextual prioritization by indicating sensitivity and business impact.

Integration Tips: Classify repositories and modules by criticality and exposure, and use classification to weight severity scoring for findings.

Verification Method: Check classification registers and verify scoring integrates classification attributes into prioritization.

Priority: Medium

6.2.10. REQ-010: Developer dashboards with personalized security metrics and trend analysis

OWASP ASVS Controls

ASVS 12.1.1

Requirement: APIs providing dashboards must enforce authentication, authorization, and secure data presentation.

Relevance: Dashboards expose sensitive security metrics per developer; API and UI must enforce access controls and secure handling of metrics.

Integration Tips: Implement per-user RBAC, restrict metric visibility to permitted scopes, and sanitize any user-provided inputs used in dashboard queries.

Verification Method: Test API endpoints for access control bypass, review role mappings, and inspect UI rendering for sensitive data leaks.

Level: L2 | Priority: High

NIST SP 800-53 Controls

AU-6

Requirement: Review and analyze audit records and report findings to support security monitoring and metrics.

Relevance: Dashboards should consume audited events and present trend analysis; AU-6 maps to creating reliable metrics from logs.

Integration Tips: Feed audited security events into metrics pipelines, ensure timestamps and user context are preserved, and implement access controls on metrics.

Verification Method: Compare dashboard metrics against raw audit logs to ensure accuracy and completeness.

Priority: Medium

ISO 27001:2022 Controls

A.7.2.2

Requirement: Employees should receive appropriate awareness and training to support information security responsibilities, enabling personalized metrics to drive improvements.

Relevance: Personalized metrics can be used to drive training and awareness; this control supports using dashboards for developer improvement.

Integration Tips: Map dashboard insights to training campaigns and provide links to remediation resources; ensure privacy-considerate presentation of developer metrics.

Verification Method: Verify links to training are available and that metrics are used in awareness programs.

Priority: Low

6.2.11. REQ-011: Issue comment threads, collaboration features, and integration with Slack/Teams notifications

OWASP ASVS Controls

ASVS 12.1.1

Requirement: External integrations (e.g., Slack, Teams) must be authenticated and use secure channels.

Relevance: Notifications and comment integrations must be securely authenticated and ensure messages don’t leak sensitive information.

Integration Tips: Use application-level tokens with minimum scopes for integrations, encrypt webhook payloads, and redact sensitive fields before sending notifications.

Verification Method: Inspect integration tokens and scopes, verify payload encryption/signing, and test for sensitive data in notifications.

Level: L2 | Priority: High

NIST SP 800-53 Controls

AU-2

Requirement: The information system provides audit records for defined events to support incident notification and collaboration.

Relevance: Issue threads and notifications generate events that must be auditable for traceability and incident investigations.

Integration Tips: Log all collaboration events, map notification events to findings, and include correlation IDs to tie notifications back to findings.

Verification Method: Review audit logs for collaboration events and confirm correlation between notifications and logged events.

Priority: Medium

ISO 27001:2022 Controls

A.13.2.1

Requirement: Policies and procedures should be established for information transfer including notifications and collaboration channels.

Relevance: Provides policy-level guidance for handling notifications and collaboration channels to avoid inadvertent data exposure.

Integration Tips: Define acceptable messaging content, retention, and redaction rules for notifications; require approval for sending high-sensitivity data through channels.

Verification Method: Review information transfer policies and sample notification content for compliance with policies.

Priority: Medium

6.2.12. REQ-012: Integration with CI/CD pipelines for pre-merge and post-merge scanning

OWASP ASVS Controls

ASVS 14.1.1

Requirement: Incorporate automated security testing into the development lifecycle and CI/CD pipelines.

Relevance: Specifies integrated security testing in CI/CD, covering pre-merge and post-merge scanning requirements.

Integration Tips: Embed static analysis, dependency scanning, and secret detection as pipeline stages and fail builds on critical findings; provide feedback in PR checks.

Verification Method: Review CI pipeline configs, confirm scans run on pre-merge and post-merge events, and validate PR blocking behavior for critical issues.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SA-11

Requirement: Require developer security testing and evaluation, including integration into build and CI/CD pipelines.

Relevance: Supports requirement to enforce security testing as part of CI/CD and developer workflows.

Integration Tips: Mandate pipeline gating for security checks, store results in centralized dashboards, and require remediation before merge for high-severity issues.

Verification Method: Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement.

Priority: High

ISO 27001:2022 Controls

A.14.2.1

Requirement: Establish secure development policies including integration of security testing into development processes and pipelines.

Relevance: Policy-level requirement mandating inclusion of security in development that informs CI/CD integration choices.

Integration Tips: Document secure development requirements and enforce them via pipeline checks; require exception handling and approval for bypasses.

Verification Method: Review secure development policy and pipeline configurations for evidence of enforcement.

Priority: Medium

6.2.13. REQ-013: OWASP Top 10 and custom security policy enforcement with customizable rules

OWASP ASVS Controls

ASVS 14.4.1

Requirement: Enforce security policies including OWASP Top 10 and provide mechanisms for custom rules and policy enforcement.

Relevance: Directly mandates policy enforcement including OWASP Top 10 and extensibility for custom rules aligning with the requirement.

Integration Tips: Provide configuration UI for custom rules, map findings to OWASP Top 10 categories, and allow policy-driven blocking or advisory actions.

Verification Method: Test custom rule creation, verify policy mapping to OWASP categories, and confirm enforcement in scans and gates.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

PL-2

Requirement: Develop and disseminate system and communications protection policies and procedures tailored to system needs.

Relevance: Supports the existence of policies and procedures that define the security rules to be enforced by the platform.

Integration Tips: Document OWASP Top 10 mapping and custom rule lifecycle; communicate policy changes to users and enforce them via automated tooling.

Verification Method: Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated.

Priority: High

ISO 27001:2022 Controls

A.5.1.1

Requirement: Management should define a set of information security policies to provide management direction and support.

Relevance: Requires management-level approval for security policies which underlie enforcement of OWASP Top 10 and custom rules.

Integration Tips: Obtain policy approval for enforced rules, ensure responsibilities for rule management are assigned, and align with compliance requirements.

Verification Method: Check for signed policy documents and traceability between policies and enforcement mechanisms.

Priority: Medium

6.2.14. REQ-014: Audit logging and immutable records of findings, decisions, and remediation history

OWASP ASVS Controls

ASVS 12.3.1

Requirement: Ensure that security-relevant events are logged with integrity and protected from tampering; maintain sufficient detail for investigations.

Relevance: Specifies log integrity and tamper protection necessary to maintain immutable records of findings and remediation history.

Integration Tips: Use append-only logs, cryptographic signing of log entries or WORM storage, and ensure access controls to prevent log modification.

Verification Method: Verify cryptographic log integrity, attempt tamper tests in controlled env, and review log access controls.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AU-2

Requirement: The information system provides audit records for the organization-defined events to support after-action review and investigations.

Relevance: Auditing findings, decisions, and remediation actions requires defined audit events and logs as per AU-2.

Integration Tips: Define audit event taxonomy for findings lifecycle events, ensure timestamps and user IDs are captured, and route logs to a tamper-resistant store.

Verification Method: Review audit event definitions, sample logs for required events, and validate retention and integrity protections.

Priority: Critical

AU-9

Requirement: Protect audit records from unauthorized access, modification, and deletion to preserve integrity of logs.

Relevance: Further enforces protection for audit records to ensure they remain immutable per requirement.

Integration Tips: Segment log storage, implement RBAC for log access, and use encryption and integrity checks for stored logs.

Verification Method: Review access lists for log storage, check encryption keys usage, and validate log retention policies.

Priority: High

6.2.15. REQ-015: Exportable compliance reports and evidence packages

OWASP ASVS Controls

ASVS 12.1.1

Requirement: APIs that export reports must enforce access controls and ensure integrity of exported evidence.

Relevance: Export APIs must be protected to prevent unauthorized access or tampering with exported compliance evidence.

Integration Tips: Require authenticated, authorized requests for exports, log export events, and sign exported packages using organizational keys.

Verification Method: Test export APIs for access control enforcement and verify signatures on exported packages.

Level: L2 | Priority: High

NIST SP 800-53 Controls

CA-7

Requirement: Establish continuous monitoring processes to produce evidence and reports of system security status.

Relevance: Continuous monitoring provides the underlying data necessary for exportable compliance reports and evidence packaging.

Integration Tips: Implement scheduled evidence exports, preserve raw logs for audits, and provide signed reports to demonstrate unaltered evidence.

Verification Method: Validate that continuous monitoring feeds into report generation and that exported reports contain verifiable signatures.

Priority: Medium

ISO 27001:2022 Controls

A.18.2.3

Requirement: Information security should be reviewed regularly and results available to demonstrate compliance.

Relevance: Requires the ability to produce evidence and review results which align with generating exportable compliance reports and evidence packages.

Integration Tips: Design reporting features to include signed evidence, timestamps, and contextual logs; map report fields to compliance requirements.

Verification Method: Produce sample evidence packages for audits and confirm completeness against compliance checklists.

Priority: High

6.2.16. REQ-016: Model management: inference API endpoints, versioning, A/B testing, performance monitoring, and drift detection

OWASP ASVS Controls

ASVS 12.1.1

Requirement: Ensure API endpoints enforce authentication, authorization, and logging to support management and auditing of model endpoints.

Relevance: Inference endpoints must be protected and logged to support model management including versioning and performance tracking.

Integration Tips: Include version identifiers in API paths or headers, enforce scoped access for model operations, and collect performance telemetry for drift detection.

Verification Method: Verify authN/Z on endpoints, confirm version headers are present in logs, and review telemetry ingestion.

Level: L2 | Priority: High

NIST SP 800-53 Controls

SA-15

Requirement: The organization ensures development processes and tools support secure design, testing, versioning and management of components such as models.

Relevance: Supports a structured approach to managing model lifecycle, versioning and testing (A/B) as part of secure development processes.

Integration Tips: Adopt model governance frameworks, keep version metadata, manage access to model artifacts, and ensure scripted promotion paths with approvals.

Verification Method: Review governance artifacts, model registries, A/B testing configs, and access controls for model artifacts.

Priority: Critical

SI-4

Requirement: Monitor the information system to detect attacks and potential misuse, including monitoring model performance and anomalies.

Relevance: Monitoring for drift and abnormal model behavior maps to SI-4’s requirement for system monitoring.

Integration Tips: Implement alerts for performance regressions, model output anomalies, and integrate monitoring with incident workflows for model roll-back.

Verification Method: Review monitoring rules, simulate drift scenarios, and confirm alerts trigger defined responses.

Priority: High

6.2.17. REQ-017: Custom model training on organization codebases with privacy-preserving controls

OWASP ASVS Controls

ASVS 14.3.1

Requirement: Protect sensitive data used for model training and avoid inclusion of secrets in training datasets.

Relevance: Prevents leakage of secrets into trained models and enforces cleaning and scanning prior to using code as training data.

Integration Tips: Scan and sanitize training corpora to remove secrets, apply redaction, and incorporate data provenance metadata to track origin and transformations.

Verification Method: Run secret scans on training datasets, validate redaction effectiveness, and review provenance logs.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

PL-4

Requirement: Define acceptable use and rules for systems handling sensitive data including restrictions for training data usage.

Relevance: Using org codebases for model training requires clear rules defining consent, allowed use, and privacy-preserving constraints.

Integration Tips: Define acceptable-use agreements, require data minimization and anonymization before training, and log consent and provenance of training data.

Verification Method: Inspect rules of behavior, check anonymization pipelines, and confirm consent records for training datasets.

Priority: Critical

ISO 27001:2022 Controls

A.8.2.3

Requirement: Ensure appropriate handling of assets including data used for training, protecting confidentiality and privacy.

Relevance: Specifies safe handling of data assets, which applies to codebases used for custom model training and privacy controls.

Integration Tips: Implement handling procedures for training data, restrict access, and apply privacy-preserving techniques like differential privacy where required.

Verification Method: Audit access to training datasets and review applied privacy-preserving measures and their tuning parameters.

Priority: High

6.2.18. REQ-018: Secure storage and handling of code snippets, tokens, keys, and analysis results (encryption, retention, redaction)

OWASP ASVS Controls

ASVS 2.4.1

Requirement: Sensitive data at rest and in transit must be protected using strong cryptography; implement key management.

Relevance: Specifies practical data protection measures (encryption, key management, redaction) for code snippets and tokens.

Integration Tips: Implement field-level redaction, encrypt databases and object storage for analysis results, and store tokens in vaults not in code or plain DB fields.

Verification Method: Inspect encryption settings, redaction logic, and confirm secrets are stored only in approved vaults with access auditing.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SC-13

Requirement: Employ cryptographic mechanisms to prevent unauthorized disclosure and modification of information.

Relevance: Mandates encryption of sensitive artifacts (tokens, keys, snippets) at rest and in transit.

Integration Tips: Encrypt data-at-rest with key management, TLS for transit, and ensure encryption keys are stored in a dedicated KMS with access controls.

Verification Method: Verify encryption configuration, test data retrieval unauthorized attempts, and review KMS access logs.

Priority: Critical

ISO 27001:2022 Controls

A.10.1.1

Requirement: A policy on the use of cryptographic controls should be developed and implemented; protect keys and sensitive data.

Relevance: Requires organizational policy guiding encryption, retention and redaction of sensitive stored data consistent with the requirement.

Integration Tips: Define retention schedules, redaction rules for stored results, and key rotation policies in alignment with the cryptography policy.

Verification Method: Review cryptography policy and sample stored artifacts for correct encryption and redaction settings.

Priority: High

6.2.19. REQ-019: Vulnerability database management and mapping to CVE/NVD feeds

OWASP ASVS Controls

ASVS 14.4.1

Requirement: Verify and manage third-party components and dependencies; ensure known vulnerabilities are tracked and remediated.

Relevance: Direct guidance to track third-party component vulnerabilities and remediate based on feeds like CVE/NVD.

Integration Tips: Automate periodic feed ingestion, implement matching algorithms to identify impacted versions, and assign remediation tickets.

Verification Method: Review feed ingestion logs, confirm CVE-to-component mappings, and check remediation ticket lifecycle.

Level: L2 | Priority: High

NIST SP 800-53 Controls

CM-8

Requirement: Maintain an inventory of system components and correlate with known vulnerabilities and feeds to manage vulnerabilities.

Relevance: Maintaining an inventory and mapping to CVE/NVD is core to managing vulnerabilities discovered via dependency scanning and component analysis.

Integration Tips: Keep automated inventories (SBOM), ingest NVD feeds, auto-correlate component versions to CVEs, and flag impacted systems.

Verification Method: Verify SBOM accuracy, correlation logs showing CVE matches, and check records of vulnerability notifications and remediations.

Priority: Critical

ISO 27001:2022 Controls

A.12.6.1

Requirement: Obtain and evaluate information about technical vulnerabilities and take appropriate measures to address them.

Relevance: Mandates operational process for vulnerability feed evaluation and measures — applies to mapping CVE/NVD into platform workflows.

Integration Tips: Define SLAs for vulnerability evaluation, maintain a feed processing pipeline, and provide visibility to stakeholders on findings.

Verification Method: Examine vulnerability management reports, SLA adherence, and evidence of action on identified CVEs.

Priority: High

6.2.20. REQ-020: Caching of analysis results for performance with cache invalidation policies

OWASP ASVS Controls

ASVS 5.1.1

Requirement: Ensure correct state management and caching strategies that do not leak sensitive data or create stale/inaccurate results.

Relevance: Addresses state and caching correctness to prevent stale or sensitive data being served in caches.

Integration Tips: Separate caches by tenancy/repo scope, implement per-item TTLs, and ensure caches are invalidated on PRs or commits that change analysis context.

Verification Method: Review cache configuration, run invalidation scenarios, and check caches for unexpectedly persisted sensitive data.

Level: L2 | Priority: Medium

NIST SP 800-53 Controls

SC-5

Requirement: Protect system availability including caching mechanisms that may affect availability and implement controls to mitigate DoS and stale data.

Relevance: Caching can affect availability and stale results; SC-5 maps to protecting availability and ensuring cache policies don’t lead to DoS or stale/inaccurate findings.

Integration Tips: Implement cache eviction, TTL, and capacity controls; protect cache endpoints from abuse and ensure invalidation on repository changes.

Verification Method: Test cache invalidation on commits, simulate heavy load and confirm DoS protections and cache limits.

Priority: Medium

CM-6

Requirement: Establish and document baseline configurations and settings for caching behaviors including invalidation policies.

Relevance: Requires documented config and baseline for caching behavior which helps ensure consistent invalidation policies and performance tuning.

Integration Tips: Document cache baselines, include recommended TTLs, and ensure configuration is part of deployment manifests with change control.

Verification Method: Verify documented cache baselines and inspect deployed configurations for alignment.

Priority: Low

6.2.21. REQ-021: Access control and RBAC for repo access, model access, and UI/API operations

OWASP ASVS Controls

ASVS 2.2.1

Requirement: Implement robust authentication and session controls and role-based access control for applications and APIs.

Relevance: Specifies implementing RBAC for application/API sessions to control access to sensitive operations like model use or repo modifications.

Integration Tips: Define roles for repo admins, reviewers, model trainers; implement session timeouts, consent-based scopes, and token revocation for role changes.

Verification Method: Test role-based access enforcement for UI and APIs and verify session handling behaviors.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AC-2

Requirement: Manage information system accounts including provisioning, de-provisioning, and role assignments.

Relevance: RBAC and account lifecycle management are necessary to control repo/model/UI/API access across the platform.

Integration Tips: Implement automated provisioning/deprovisioning tied to identity sources, enforce least privilege and role reviews, and maintain account inventories.

Verification Method: Review account provisioning logs, role assignments and deprovision records; perform access reviews.

Priority: Critical

ISO 27001:2022 Controls

A.9.1.2

Requirement: Users should only be provided access to network services necessary to perform their role.

Relevance: Supports least privilege and RBAC by restricting access to only necessary services and platform functions.

Integration Tips: Segment admin functions into separate services and apply RBAC at service and API levels; enforce network segmentation where appropriate.

Verification Method: Inspect access policies and run access tests to confirm role-appropriate service access.

Priority: High

6.2.22. REQ-022: Rate limiting, abuse prevention, and API authentication/authorization

OWASP ASVS Controls

ASVS 12.1.1

Requirement: Ensure APIs enforce authentication, authorization, rate limiting and input/output validation to prevent abuse.

Relevance: Directly applicable: enforces rate limiting and abuse prevention controls on APIs used for inference and scanning.

Integration Tips: Implement per-API and per-user throttle limits, API keys with quotas, usage monitoring and automated throttling on anomalous behavior.

Verification Method: Run load and abuse tests to confirm throttling, inspect auth/authorization policies and quota enforcement logs.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

IA-5

Requirement: Manage authenticators and credentials and protect against misuse and brute-force or credential stuffing attacks.

Relevance: Helps prevent abuse by ensuring robust authenticator management and protections against credential-based attacks on APIs.

Integration Tips: Enforce strong credential policies, lockout thresholds, multi-factor authentication for privileged API access, and monitor for credential misuse.

Verification Method: Review authenticator policies, test brute-force protections and check account lockout logs.

Priority: High

ISO 27001:2022 Controls

A.13.1.1

Requirement: Networks should be managed and controlled to protect information in systems and applications, including controls to mitigate abuse and overload.

Relevance: Network-level controls complement API rate limiting to prevent overload and abuse impacting service availability.

Integration Tips: Apply rate limiting at edge proxies, use WAF and DDoS protections, and configure network ACLs to mitigate abusive traffic sources.

Verification Method: Review edge proxy configs, WAF rules, and DDoS protection telemetry during simulated abuse.

Priority: Medium

6.2.23. REQ-023: Support for on-premises or isolated VPC/SaaS deployment models and data residency controls

OWASP ASVS Controls

ASVS 14.1.1

Requirement: Support secure deployment models including separated environments, hardened infrastructure and data residency controls.

Relevance: Specifies secure deployment approaches and supports differentiating on-prem/VPC or SaaS with data residency controls.

Integration Tips: Offer configurable deployment templates for on-prem and VPC isolate; implement data locality tagging and policies to restrict cross-border data movements.

Verification Method: Review deployment manifests for isolation features and validate data residency enforcement in sample deployments.

Level: L2 | Priority: High

NIST SP 800-53 Controls

SC-7

Requirement: Monitor and control communications at external boundaries and implement deployment-specific network segmentation such as VPC isolation.

Relevance: Maps to implementing VPC isolation and boundary protections for on-prem and isolated deployments.

Integration Tips: Use network segmentation, private endpoints, and peering controls to enforce isolation; restrict management interfaces to secure management networks.

Verification Method: Pen-test network boundaries, inspect VPC configs, and verify management plane access restrictions.

Priority: High

ISO 27001:2022 Controls

A.8.1.3

Requirement: Define and communicate acceptable use of organizational assets including deployment choices affecting data location and handling.

Relevance: Policy-level control to ensure data residency and deployment choices are authoritative and administratively enforced.

Integration Tips: Provide customers with clear acceptable-use and data residency options in contracts and deployment docs; enforce via deployment automation.

Verification Method: Review contractual terms, deployment records and customer configuration options for compliance to stated residency.

Priority: Medium

6.2.25. REQ-025: Monitoring, alerting, and incident response for platform security (SIEM integration)

OWASP ASVS Controls

ASVS 12.3.1

Requirement: Ensure detection and response capabilities are in place with integrated logs and monitoring to support incident response.

Relevance: Requires application-level logging and integration with monitoring tools to support SIEM and IR activities for the platform.

Integration Tips: Ensure application logs include context (user, repo, model version), push logs to SIEM, and configure runbooks for prioritized alerts.

Verification Method: Audit logs for required context fields, verify SIEM correlation rules and test full incident response flow.

Level: L2 | Priority: High

NIST SP 800-53 Controls

SI-4

Requirement: Monitor the information system to detect attacks and potential misuse and send alerts to administrators.

Relevance: Core to SIEM integration: requires monitoring and alerting to detect anomalies, misuse, and security events in the platform.

Integration Tips: Forward application and infrastructure logs to SIEM, implement correlation rules for key events (auth failures, suspicious model queries), and configure alerting thresholds.

Verification Method: Validate SIEM ingests logs, simulate incidents and confirm alerts, and review incident ticket creation.

Priority: Critical

ISO 27001:2022 Controls

A.16.1.5

Requirement: Establish responsibilities and procedures to ensure a quick, effective and orderly response to information security incidents.

Relevance: Defines incident response processes which must be integrated with monitoring/alerting and SIEM to handle platform security events.

Integration Tips: Document incident response playbooks for platform-specific incidents, integrate SIEM alerts into incident workflows and perform regular exercises.

Verification Method: Review incident response plans, confirm SOPs for SIEM alerts, and evaluate results of tabletop or live exercises.

Priority: High

6.3. Cross-Functional Security Controls

The following controls apply globally across all system components:

Logging and Auditability

Description: Comprehensive logging of authentication events, API calls, model inferences, scans, PR actions, and remediation decisions with protections for integrity and retention.

Applies to: Repository integration, LLM inference API, Static analysis, Dependency scanning, Secret detection, Auto PRs, Dashboards, Notifications, CI/CD integration, Model training, Storage

Implementation Guidance: Implement structured, tamper-evident logs with correlation IDs, forward to centralized SIEM, protect logs with RBAC and encryption, and retain logs per compliance retention schedules.

Encryption and Key Management

Description: Encrypt sensitive data at rest and in transit, and manage keys using a centralized KMS with rotation and access controls.

Applies to: Token storage, Code snippets, Model artifacts, Databases, Backups, Exported evidence

Implementation Guidance: Use TLS for transport, AES-256 for storage, KMS-backed envelope encryption, enforce key rotation policies, and restrict KMS access via IAM roles.

Access Control and RBAC

Description: Centralized role-based access control for repository access, model operations, APIs and UI functions with account lifecycle management.

Applies to: Repo access, Model management, Admin interfaces, API endpoints, Dashboards

Implementation Guidance: Integrate with enterprise identity providers, implement least privilege roles, periodic access reviews, and automated provisioning/deprovisioning.

Data Protection and Privacy

Description: Policies and technical controls for data handling including consent, opt-in/opt-out for training, redaction, anonymization, and data residency enforcement.

Applies to: Custom model training, Storage of code and snippets, Exported reports, Cross-border deployments

Implementation Guidance: Capture consent metadata, tag datasets with residency and sensitivity, apply anonymization/differential privacy for training, and enforce opt-out filters in pipelines.

6.4. Requirements Traceability Overview

This section demonstrates complete traceability from high-level requirements through threats to security controls and verification methods.

Coverage Summary: Traceability matrix contains 28 requirements. 28 requirements (100.0%) linked to threats. 25 requirements (89.3%) mapped to security controls (OWASP ASVS, NIST SP 800-53, ISO 27001). Coverage: Partial.

Sample Traceability Mappings

The following table shows traceability for high-priority requirements:

Req ID	Requirement	Threats	Security Controls	Standards	Priority	Verification
REQ-001	Repository integration with GitHub, GitL…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens.
REQ-003	Provide an LLM-based code review engine …	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers.
REQ-004	Perform static code analysis (SAST) and …	10 threats	2 controls	NIST, OWASP	Critical	Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates.
REQ-005	Dependency scanning that identifies libr…	9 threats	3 controls	ISO27001, NIST, OWASP	Critical	Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI.
REQ-006	Secret detection across code, config fil…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI.
REQ-008	Support automatic pull request creation …	7 threats	3 controls	ISO27001, NIST, OWASP	Critical	Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs.
REQ-009	Provide severity scoring and risk assess…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans.
REQ-013	Integrate with CI/CD pipelines (e.g., Gi…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement.
REQ-015	Provide OWASP Top 10 checks and allow cr…	7 threats	3 controls	ISO27001, NIST, OWASP	Critical	Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated.
REQ-016	Maintain immutable audit logs of all ana…	10 threats	3 controls	NIST, OWASP	Critical	Review audit event definitions, sample logs for required events, and validate retention and integrity protections.

Showing 10 of 28 requirements. See Appendix D for complete traceability matrix.

Traceability Statistics

Total Requirements Tracked: 28
Requirements Linked to Threats: 28 (100.0%)
Requirements Mapped to Controls: 25 (89.3%)
Average Controls per Requirement: 2.6
Control Distribution by Standard:
- NIST SP 800-53: 27 controls
- OWASP ASVS: 26 controls
- ISO 27001: 21 controls
Verification Coverage: 100% (all requirements have verification methods)

7. AI/ML Security Requirements

This section addresses security requirements specific to artificial intelligence and machine learning components within the system. AI/ML systems introduce unique security challenges including prompt injection attacks, data poisoning, model theft, adversarial inputs, and bias vulnerabilities. This analysis identifies AI/ML components, assesses their security risks, and prescribes specialized controls to protect both the AI systems themselves and the data they process.

7.1. AI/ML Components Detected

This section identifies all AI/ML components within the system that require specialized security controls.
1. LLM-based Code Review Engine: Utilizes fine-tuned large language models for analyzing code commits, identifying vulnerabilities, and providing remediation suggestions.
2. Static Code Analysis Engine: Employs machine learning techniques for vulnerability detection and pattern recognition, including dependency scanning for known CVEs.
3. AI-generated Remediation Suggestions: Provides contextual explanations and code snippets for identified vulnerabilities, utilizing AI to enhance developer understanding and actionability.
4. Model Management System: Responsible for managing model inference, versioning, and custom training based on organization-specific codebases.

7.2. AI/ML Threat Model

Component	Identified Threats
LLM-based Code Review Engine	- Prompt injection
	- Data leakage through training data
	- Adversarial inputs
Static Code Analysis Engine	- Model poisoning
	- Data leakage of PII and sensitive information
AI-generated Remediation Suggestions	- Output filtering vulnerabilities
	- Misleading or incorrect suggestions
Model Management System	- Model inversion attacks
	- Supply chain vulnerabilities

7.3. AI/ML Security Controls

LLM-based Code Review Engine

Prompt Injection Prevention: Implement strict input validation for all prompts sent to the model. Use whitelists for acceptable input patterns.
Output Filtering and Sanitization: Apply filtering mechanisms to sanitize outputs before displaying them to users, ensuring that potentially harmful content is removed.

Static Code Analysis Engine

Input Validation for AI Inputs: Enforce rigorous validation on all inputs to the static analysis engine to prevent injection attacks and malicious inputs.
Data Leakage Prevention: Ensure that proprietary or sensitive code is never included in training data. Use techniques like data anonymization.

AI-generated Remediation Suggestions

Contextual Output Filtering: Implement logic to ensure AI-generated suggestions are contextually relevant and accurate, reducing the risk of misleading fixes.
Model Access Controls: Enforce strict access controls to limit who can access and utilize the model for generating suggestions, preventing misuse.

Model Management System

Model Versioning and Rollback Capabilities: Maintain a comprehensive versioning system that allows for rollback to previous model versions in case of detected issues or vulnerabilities.
Supply Chain Security for Models: Implement checks and integrity verification for all models sourced externally to ensure they have not been tampered with.

7.4. Integration with Existing Security Controls

AI controls should be integrated into existing security practices such as the OWASP Top 10 compliance checks, ensuring that all AI-related components are assessed for vulnerabilities. Additionally, the use of standard authentication and access control measures (like OAuth and RBAC) can complement AI-specific controls to create a more robust security posture.

7.5. AI/ML Monitoring Requirements

Monitoring Area	Description
Prompt Injection Monitoring	Continuously monitor inputs for signs of prompt injection attempts.
Adversarial Input Monitoring	Implement detection systems for unusual patterns that indicate adversarial attacks on the AI model.
Model Performance Monitoring	Regularly assess model outputs for drift and degradation in performance.
Data Leakage Monitoring	Monitor logs for unauthorized access attempts to sensitive data, including code snippets and tokens.

8. Compliance Requirements

This section identifies regulatory and legal compliance obligations applicable to the system based on data types, geographic scope, industry sector, and business operations. Compliance requirements drive specific security controls, data handling procedures, audit capabilities, and privacy protections. Non-compliance can result in significant legal penalties, reputational damage, and business disruption. This analysis maps applicable regulations to specific security requirements and operational procedures.

8.1. Applicable Regulations

The AI-powered code review and security analysis platform processes various categories of sensitive data, including source code, authentication tokens, and potential proprietary information. The regulations identified stem from the types of data handled, the geographic scope of operations, and the nature of the industry, particularly focusing on data protection and security compliance. Compliance requirements directly impact security controls, data handling procedures, and operational processes to ensure that the platform adheres to legal obligations. The following table outlines the applicable regulations and the reasons for their applicability:

Regulation	Applicability Reason
GDPR	Applies because the system processes personal data of EU residents, which includes any identifiable information.
CCPA	Applies as the platform may collect and process personal data of California residents, providing rights related to their data.
HIPAA	Relevant if health-related information is processed, particularly if the code includes health-related data or services.
PCI-DSS	Applicable if payment card information is processed or stored in the platform, including tokens and API keys.
SOX	Relevant for financial data handling and ensuring the integrity of financial reporting and audit trails.
COPPA	Applies if the platform collects data from children under 13, necessitating parental consent.
Data Residency Laws	Relevant if data is stored or processed in specific jurisdictions that impose restrictions on data movement.

8.2. Compliance Controls by Regulation

CCPA

Opt-Out Mechanism: Provide users the ability to opt-out of data selling.
Disclosure Requirements: Inform users about data collection and usage practices.
Access Requests: Facilitate user requests for data access and deletion.

HIPAA

PHI Protection: Implement safeguards to protect personally identifiable health information.
Business Associate Agreements: Ensure contracts with third-party vendors handling PHI include HIPAA compliance clauses.
Training: Conduct regular staff training on HIPAA regulations.

PCI-DSS

Encryption: Protect cardholder data through strong encryption methods.
Access Control: Implement strict access controls for systems storing cardholder data.
Monitoring and Testing: Regularly test security systems and processes.

SOX

Audit Trails: Maintain comprehensive audit logs of all transactions and access to data.
Data Integrity Controls: Ensure controls are in place to maintain data integrity.
Regular Audits: Conduct regular compliance audits.

COPPA

Parental Consent: Implement mechanisms to obtain verifiable parental consent.
Data Deletion Policy: Ensure the ability to delete children’s data upon request.

Data Residency Laws

Data Localization: Store data within specified geographic boundaries as required.
Compliance Monitoring: Regularly review compliance with local data residency laws.

8.3. Data Subject Rights

Right	Description
Right to Access	Users can request access to their personal data.
Right to Rectification	Users can request corrections to inaccurate personal data.
Right to Erasure	Users can request deletion of their personal data.
Right to Object	Users can object to the processing of their data under certain conditions.
Right to Data Portability	Users can request their data in a structured format for transfer.

8.4. Privacy Requirements

Consent: Users must provide explicit consent for data collection and processing activities.
Privacy Notice: A clear privacy policy must be provided, detailing data collection, usage, sharing, and user rights.
Data Usage Policies: Define how data will be used, including analytics and third-party sharing.

8.5. Audit and Monitoring Requirements

Logging: Maintain detailed logs of access to personal data and security events for accountability.
Monitoring: Implement continuous monitoring of data access and processing activities for compliance.
Incident Response: Establish an incident response plan to address data breaches and security incidents.

8.6. Data Handling Rules

Retention: Data should only be retained as long as necessary for its intended purpose, with defined retention periods.
Deletion: Implement secure deletion protocols for personal data no longer required.
Data Security: All sensitive data must be secured through encryption and access controls.

8.7. Compliance Risk Assessment

The compliance risk assessment identifies potential risks associated with regulatory compliance and security controls that must be addressed to mitigate these risks.

Key Compliance Risks:

Mismanagement of personal data leading to GDPR and CCPA violations.
Inadequate protection of health-related information, risking HIPAA non-compliance.
Failure to protect payment information, leading to PCI-DSS violations.
Insufficient audit trails and documentation, risking SOX compliance breaches.

9. Security Architecture Recommendations

This section provides comprehensive security architecture guidance that integrates security controls into the system’s technical design. Security architecture defines how security principles, controls, and patterns are applied across system components to create a cohesive, defense-in-depth security posture. The recommendations address architectural principles, component-level controls, data protection strategies, and third-party integration security to ensure security is built into the system design.

9.1. Architectural Security Principles

Architectural security principles provide the foundational philosophy guiding all security design decisions. These principles ensure a consistent security posture across all system components and guide the selection and implementation of security controls, balancing protection, usability, and performance for an AI-powered code review and security analysis platform.

Zero Trust Architecture principles: Never trust, always verify — all requests (users, services, devices) are authenticated and authorized regardless of network location or origin. This reduces implicit trust assumptions and limits lateral movement if any component is compromised.
Defense in Depth: Multiple, independent layers of controls (network, application, data, identity, monitoring) reduce the chance a single failure leads to a breach; each layer compensates for weaknesses in others.
Principle of Least Privilege: Grant users and services only the minimum privileges required to perform tasks, both for repository access and model operations; reduces blast radius of credential compromise.
Secure by Default / Secure by Design: Services should ship with secure defaults (e.g., strong crypto, logging, RBAC) and security decisions considered at design time to avoid retrofitting.
Separation of Duties: Split critical functions across roles and systems (e.g., model training vs. inference, audit management vs. remediation approvals) to prevent conflicts of interest and reduce risk of insider abuse.
Fail Secure (Fail Closed): In failure scenarios, systems should deny sensitive operations rather than fail open; e.g., if an auth check cannot be completed, deny access.
Complete Mediation: Every access request must be checked against current policy (no caching of authorization decisions beyond their valid TTL) to ensure policy changes take immediate effect.
Defense-in-Depth for Data: Combine encryption, access controls, redaction and data minimization for sensitive artifacts (code snippets, tokens, PII) so data compromise is hard to exploit.
Model Governance & Responsible ML: Govern model lifecycle with versioning, testing, provenance, drift detection, and human-in-the-loop safeguards for high-impact suggestions.
Privacy by Design: Treat customer code and any PII in it as sensitive; embed consent capture, opt-in/opt-out training, anonymization and minimization into data pipelines.
Auditability & Tamper-Evident Logging: Capture rich, correlated telemetry for all actions (scans, PRs, model inferences) and protect logs with append-only storage and cryptographic integrity to support forensics and compliance.
Resilience and Availability: Design for graceful degradation and abuse protection (rate limiting, quotas, DDoS mitigation) to maintain availability under load or attack.

9.2. Component-Level Security Controls

Edge/API Gateway

Required Controls:

OAuth2/OIDC integration for user and repository authorization
Mutual TLS (mTLS) for backend service communication where supported
JWT validation and token introspection with short lifetimes and refresh policies
Request rate limiting and per-actor quotas
Web Application Firewall (WAF) and DDoS protection integration
Input validation and payload size limits for uploads (e.g., code diffs)
IP allow/deny lists for management/administrative endpoints
Centralized request logging with correlation IDs and structured telemetry
TLS 1.3 enforcement and strict cipher suites
RBAC enforcement at the gateway for coarse-grained routing decisions

Recommended Patterns:

API Gateway with OAuth2, OIDC and scoped tokens
Edge WAF and API layer DDoS protection (e.g., managed WAF + CDN)
Short-lived JWT + refresh tokens with centralized session revocation endpoint
Edge-based rate limiting and per-tenant quotas
Canary routing for new model versions and A/B testing via gateway rules

Frontend User Interface

Required Controls:

Enforce authentication via SSO (SAML/OIDC) with MFA for admin roles
Per-user RBAC and permission checks on every API call
Content Security Policy (CSP), X-Frame-Options, and secure cookies (HttpOnly, Secure, SameSite)
Input validation and output encoding to prevent XSS
Session management with short idle/absolute timeouts and revocation support
Client-side redaction/obfuscation of secrets when displaying suggestions
Client telemetry with user consent and privacy-preserving defaults

Recommended Patterns:

Single Page App (SPA) served via CDN with strict CSP and Subresource Integrity
Use OAuth/OIDC implicit or auth-code + PKCE flow delegated to gateway
Role-based UI rendering with server-side authorization checks
Frontend feature flags for progressive exposure of auto-fix capabilities

Application Services

Required Controls:

Strong service-to-service authentication (mTLS or short-lived service tokens)
Fine-grained RBAC for repository operations, PR creation, approvals
Input validation and sanitization for all external inputs (webhooks, code uploads)
Audit logging for all actions (scans triggered, PRs created, approvals, model training requests)
Secrets vault integration for storing integration tokens and keys
Rate limiting and back-pressure handling for scan orchestration
Secure CI/CD integration using ephemeral credentials and least-privilege runners

Recommended Patterns:

Microservices with API Gateway fronting and internal service mesh (mTLS, network policies)
Workflow orchestration via message queues with signed job tokens and idempotency keys
Use of Dedicated Service Accounts per integration with least privilege
Outbound webhook signature verification for incoming repo events

AI Analysis Engine

Required Controls:

Isolated execution contexts for analysis jobs with process/file system separation
Input sanitization and secret detection prior to passing code into models
Redaction of sensitive data from model prompts/responses and stored artifacts
Resource quotas per tenant plus rate limiting on inference calls
Logging of model inputs/outputs with privacy-preserving sanitization and provenance metadata
Hardened containers/VMs for model runtime with runtime integrity checks
Dependency and SAST tool sandboxing; scanned tools run with minimal privileges

Recommended Patterns:

Dedicated inference cluster with namespace isolation per tenant (Kubernetes namespaces, VPCs)
Use of container-native security (gVisor, Kata Containers) for additional isolation
Pre-processing pipeline: secret-scan -> redaction -> tokenize -> inference -> post-process
Hybrid inference: managed-host for public SaaS, on-prem model hosting for private deployments

Model Management

Required Controls:

Model artifact repository with integrity checks (hashing, signatures)
Model versioning, promotion gates, and rollback capability
Access controls for model training, deployment, and download
Audit trail for training data provenance and model changes
Performance monitoring, drift detection and alerting
Isolation for customer-specific model training (tenant VPCs or on-prem training nodes)
Controls to prevent leakage of training data in inference (privacy-preserving techniques)

Recommended Patterns:

Model registry with signed artifacts and immutable metadata (e.g., MLflow + artifact signing)
CI/CD for models with automated tests, security checks and manual approval for production models
A/B testing routing layer with canary percentages and automatic rollback on regressions
Differential privacy / federated learning options for sensitive training datasets

Data Storage & Persistence

Required Controls:

Field-level encryption for sensitive fields (tokens, secrets, PII)
KMS-managed envelope encryption and BYOK options for enterprise customers
Immutable audit log storage (WORM) for findings and remediation history
Access controls and DB encryption at rest (TDE) and network isolation
Data classification tagging and per-tenant data partitions
Backup encryption and secure backup lifecycle management
Secure deletion workflows and verified erasure for deletion requests

Recommended Patterns:

Encrypted object storage for code artifacts with KMS envelope encryption
Database encryption (TDE + column-level encryption) plus separate DB roles for access
Use of immutable object storage for audit logs with append-only retention policies
Tenant-scoped storage buckets or DB schemas for data separation

External Integrations

Required Controls:

Secure credential storage for integration tokens (vault)
Verification of webhook payload signatures, replay protection and throttling
Least-privilege OAuth scopes and periodic token rotation
Monitoring, logging and alerting for integration failures and anomalous activity
Isolation of connectors and outbound network egress controls

Recommended Patterns:

Connector microservices running in restricted network zones with egress controls
Use of ephemeral credentials and OAuth tokens bound to connector instances
Circuit breakers for unreliable third-party endpoints and retry/backoff policies
Central policy engine to control what integrations may access and which events can be forwarded

9.3. Data Protection Strategy

Data Classification: Public, Internal, Confidential, Restricted

Public: Non-sensitive documentation, marketing materials, generic scan metrics without tenant-identifying info.
Internal: Operational telemetry, non-sensitive aggregated metrics and dashboards.
Confidential: Repository metadata, scan job metadata, aggregated scan results that are tenant-identifiable, model telemetry.
Restricted: Source code snippets/diffs, secrets (API keys, tokens), PII contained in code/config, customer training datasets, KMS keys and BYOK materials, audit evidence used in compliance packages.

Encryption Requirements:

Data in transit: TLS 1.3 with strong ciphers (AEAD suites); enforce TLS 1.3+; perfect forward secrecy (ECDHE). For internal service-to-service communication prefer mTLS (TLS 1.3).
Data at rest: AES-256-GCM for object storage and DB field-level encryption; database Transparent Data Encryption (TDE) with AES-256.
Key management: KMS-based envelope encryption; use RSA-4096 or ECC P-384/ECDSA for key signing where applicable. Support BYOK (customer-managed keys) through secure import or dedicated KMS federation.
Log integrity: HMAC-SHA256 or SHA-512 signatures for log blocks, with periodic notarization (e.g., Merkle tree or blockchain-backed timestamping for high-assurance tenants).

Retention Policies:

Default retention: Keep analysis artifacts and scan metadata for 90 days for operational use; aggregated metrics indefinitely (anonymized).
Compliance retention: Allow configurable retention per tenant to meet regulatory obligations (e.g., GDPR minimum necessary period, ability to specify longer retention for audit).
Audit/evidence: Retain signed audit/evidence packages for at least the period required by the customer’s compliance (default 7 years for regulated customers if requested).
Secrets/exposed artifacts: If secrets are detected and recorded in the vault for remediation workflows, mark them restricted and retain only for 30 days unless required longer by incident response — provide automated purge workflows.
Backups: Encrypted backups retained according to policy (default 90 days), with longer cold archives as required by customers (WORM support/append-only for compliance).

Handling Procedures:

Ingress: Code snippets are ingested via authenticated webhooks/CI connectors; pre-process immediately with secret detection and classification. Any detected secret triggers immediate customer notification and optional blocking.
Access: Enforce RBAC and least privilege for read/write access to stored artifacts; require just-in-time elevation for sensitive operations and log all access.
Transmission: Always over TLS 1.3; for internal control planes use mTLS and internal network segmentation (private subnets/VPCs).
Storage: Apply tenant-scoped segmentation and encryption at rest; redaction of secrets and PII before persisting to long-term storage unless customer explicitly opts into storing raw artifacts for diagnostics (explicit consent required).
Training Data: Respect opt-in/opt-out tags; sanitize and redact secrets/PII before inclusion; use differential privacy or federated learning for aggregate improvements.
Deletion: Implement cryptographically verifiable deletion for object stores and databases; honor customer deletion requests and propagate deletion to backups within a defined SLA (e.g., 30-90 days depending on backup schedule).
Export: Exports of compliance evidence must be signed and transmitted over secure channels; require authorization and log export events.
Incident Response: For leaked secrets or unauthorized access, immediate revocation of affected tokens/keys (via KMS and connectors), rotation workflows, and automatic creation of incident tickets.

9.4. Third-Party Integration Security

GitHub / GitLab / Bitbucket

Security Requirements:

OAuth app with minimum scopes and explicit consent per repository
Webhook payload signature verification (HMAC) and replay protection
Storage of integration tokens in vault with rotation
Granular per-repo permission mapping and token-scoped access control

Risk Assessment: High - direct access to source code and ability to create PRs; compromise could expose proprietary code or allow unauthorized code changes.

Recommended Controls:

Per-repo least-privilege OAuth scopes and periodic scope review
Validate webhook signatures and reject unsigned requests
Rate limit webhook processing and apply payload size limits
Store tokens in vault (KMS-backed) and rotate automatically; use short-lived installation tokens where possible
Map repo identities to internal RBAC and require admin approval for enabling integrations

CVE/NVD Feeds

Security Requirements:

Secure ingestion over HTTPS with feed integrity verification (signed feeds where available)
Validation and normalization of feed data before mapping to SBOM/component inventory
Timestamp validation and replay protection for feed updates

Risk Assessment: Medium - feed tampering or poisoning could produce false positives/negatives in vulnerability mapping.

Recommended Controls:

Validate feed checksums/signatures and use mirror checks from reputable sources
Maintain an independent verification and staging pipeline for new feed entries
Alert on anomalous feed patterns and enable manual vetting for critical CVEs

Slack

Security Requirements:

Use app-level tokens with least privileged scopes
Verify webhook/event signatures and use dedicated channels for notifications
Redact sensitive content before sending notifications

Risk Assessment: Medium - notifications may leak sensitive info if not redacted; token compromise could be used for social engineering.

Recommended Controls:

Store Slack tokens in vault and rotate periodically
Redact secrets and sensitive code fragments from messages
Restrict channels used for critical alerts and require approval to forward full findings externally

Microsoft Teams

Security Requirements:

Use dedicated app registrations with OAuth and least privileged scopes
Validate outgoing connector authenticity
Redaction of sensitive data in notifications

Risk Assessment: Medium - similar to Slack, risk of leakage and token misuse.

Recommended Controls:

Token vaulting and rotation
Teams channel access control and scoped connectors
Rate limiting and content filters on notifications

CI/CD Systems (GitHub Actions, Jenkins, GitLab CI)

Security Requirements:

Use ephemeral, least-privilege runner credentials and short-lived tokens
Validate job origin and image provenance for runner execution
Integration only via secure endpoints and scoped tokens

Risk Assessment: High - CI/CD agents can execute arbitrary code; compromised CI could weaponize auto-PRs or auto-fixes.

Recommended Controls:

Use ephemeral credentials bound to specific jobs and restrict runner networks
Run SAST/dependency scanning in isolated sandboxed runners
Enforce signed commits / require verified signatures for auto-applied changes
Integrate PR gating and require human approval for high-risk auto-fixes

SSO Providers (SAML/OIDC Identity Providers)

Security Requirements:

Enforce MFA for privileged roles
Implement strict redirect URI validation and token lifetime policies
Audit and log SSO assertions and authentication flows

Risk Assessment: High - SSO compromise could yield broad platform access.

Recommended Controls:

Enforce SSO with conditional access policies and MFA
Periodic access reviews and automated deprovisioning workflows
Use SCIM for automated provisioning and disable stale accounts

Managed Model Hosts / GPU Providers

Security Requirements:

Network isolation for model hosts and secure management plane access
Customer data handling agreements for datasets used in custom training
Ensure hypervisor/container isolation and secure image provenance

Risk Assessment: High for multi-tenant hosted models — risk of data leakage, model theft, or cross-tenant inference leakage.

Recommended Controls:

Tenant isolation via VPCs, dedicated instances or hardware enclaves for sensitive training
Encrypted disk and in-memory protections (where available)
Strict access control to training logs and artifacts; sign models before export

SIEM / Monitoring Providers

Security Requirements:

Secure log forwarding (TLS) with authentication and integrity checks
Role-based access to SIEM dashboards and retention configurations
Ensure log redaction for sensitive fields before forward

Risk Assessment: Medium - logs contain sensitive metadata that could be abused if leaked.

Recommended Controls:

Use secure collectors with vetting and filtering rules
Implement selective forwarding and redaction of PII/secrets
Validate retention and access controls in SIEM

Backup / Archival Providers

Security Requirements:

Encrypted backups with KMS control and WORM/append-only options for audit artifacts
Access controls and audit logging for backup retrieval and restores

Risk Assessment: Medium - backups hold full datasets; compromise could lead to exfiltration of historical code/artifacts.

Recommended Controls:

Enforce BYOK for enterprise customers and access audit trails
Use immutable backup storage and monitor backup access
Periodic restoration tests for integrity and secure deletion tests

(End of document)

10. Implementation Roadmap

This section provides a prioritized, phased approach for implementing the security controls identified throughout this analysis. The roadmap organizes security measures into logical phases based on risk, dependencies, and resource availability, ensuring critical security gaps are addressed first while building a foundation for comprehensive security coverage.

10.1. Prioritization Framework

Provide a comprehensive explanation of the prioritization approach used to organize security controls into implementation phases. Begin with a paragraph explaining why prioritization is critical for effective security implementation, then explain each criterion:

Prioritization of security controls is critical to ensure that resources are allocated efficiently, addressing the most pressing security threats and compliance requirements first. It is essential to balance immediate risk mitigation with long-term strategic goals, taking into account technical and resource constraints. This structured approach ensures that critical vulnerabilities are addressed promptly while setting the stage for ongoing security enhancements.

Prioritization Criteria:

Risk Level: Controls addressing critical and high-risk threats (identified through threat modeling) are prioritized first
Compliance Deadlines: Regulatory requirements and compliance deadlines influence immediate priority
Technical Complexity: Controls requiring foundational infrastructure are implemented early to enable subsequent controls
Dependencies: Controls that other security measures depend upon are prioritized accordingly
Resource Availability: Implementation considers the availability of skilled personnel, tools, and budget
Business Impact: Controls protecting business-critical functions and data receive higher priority

These criteria work together to create a logical implementation sequence that balances security needs with practical constraints, ensuring that the most critical issues are addressed first while laying the groundwork for future improvements.

10.2. Phased Implementation Plan

Phase: IMMEDIATE

Timeline: 0-1 months

Rationale: This phase focuses on addressing critical vulnerabilities and compliance blockers that pose immediate risks to the system’s security and operational integrity.

Controls to Implement:

Harden authentication and token management, including short-lived tokens and JWT validation
Implement basic encryption for sensitive data both at rest and in transit
Address critical vulnerabilities in the system as identified in threat modeling
Establish essential access controls and secure OAuth integrations

Dependencies:

Availability of skilled security engineers to implement authentication and encryption mechanisms

Phase: SHORT-TERM

Timeline: 1-3 months

Rationale: These controls build upon immediate security measures, focusing on improving access control adjustments and ensuring that logging and API security mitigate identified threats effectively.

Controls to Implement:

Enhance user authentication through comprehensive multi-factor authentication
Deploy role-based access controls across the admin dashboard
Implement comprehensive logging and monitoring for all administrative actions
Strengthen API security with input validation and HTTPS protocols
Begin encryption for all sensitive data at rest

Dependencies:

Completion of TLS Implementation
Completion of multi-factor authentication

Phase: MEDIUM-TERM

Timeline: 3-6 months

Rationale: This phase introduces more advanced security controls that require additional planning and resource allocation, focusing on threat detection and third-party security audits.

Controls to Implement:

Deploy advanced threat detection systems to identify and mitigate potential security incidents
Automate security testing within development pipelines to ensure continuous security validation
Conduct third-party security audits to validate security posture and compliance
Enhance data protection mechanisms, including data masking and tokenization

Dependencies:

Completion of logging and monitoring systems
Availability of third-party audit services

Phase: LONG-TERM

Timeline: 6-12 months

Rationale: These strategic initiatives aim to enhance security maturity and integrate advanced AI/ML security controls, ensuring a robust security posture over the long term.

Controls to Implement:

Develop and implement security maturity enhancements across the organization
Integrate advanced AI/ML security controls into the platform for proactive threat detection
Conduct comprehensive penetration testing to identify and mitigate potential vulnerabilities
Launch security awareness programs to educate staff on best practices and emerging threats

Dependencies:

Completion of advanced threat detection systems
Availability of resources for security awareness training

Phase: ONGOING

Timeline: Continuous

Rationale: Continuous activities ensure ongoing security maintenance and readiness to respond to incidents as they arise.

Controls to Implement:

Maintain continuous security monitoring and alerting through integrated SIEM solutions
Implement regular patch management processes to keep systems updated against vulnerabilities
Conduct periodic compliance audits to ensure adherence to regulatory requirements
Maintain incident response readiness with regular drills and updates to response plans

Dependencies:

Availability of skilled personnel for continuous monitoring and incident response
Integrated tools for SIEM and patch management

10.3. Resource Requirements

Skills: Security engineers, Security architects, Web developers, Compliance specialists

Recommended tools: SIEM solutions for logging and monitoring, Vulnerability scanners for testing, Encryption libraries for data protection, API management tools for secure interfaces

Estimated time effort: Approximately 3-6 months for initial phases, with ongoing efforts extending resources as per system complexity and requirements.

11. Verification and Testing Strategy

11.1. Testing Approach

Integrate security testing throughout the software development lifecycle (SDLC) with an emphasis on continuous security practices. Balance automated scanning with manual evaluations to prioritize high-risk areas based on business impact, adhering to shift-left security principles by incorporating security testing earlier and continuously. This approach ensures that vulnerabilities are identified and remediated as early as possible, minimizing risks and enhancing overall code security.

11.2. Testing Methods

Method	Frequency	Tools
STATIC APPLICATION SECURITY TESTING (SAST)	Every commit/build	SonarQube, Semgrep, Checkmarx, CodeQL
DYNAMIC APPLICATION SECURITY TESTING (DAST)	Nightly/weekly	OWASP ZAP, Burp Suite, Acunetix
DEPENDENCY SCANNING	Every build	Snyk, Dependabot, OWASP Dependency-Check
SECRETS SCANNING	Every commit	TruffleHog, GitLeaks, GitHub Secret Scanning
CONTAINER/INFRASTRUCTURE SCANNING	Every deployment	Trivy, Clair, Prowler, ScoutSuite
PENETRATION TESTING	Quarterly or before major releases	Custom scripts, Metasploit, Burp Suite Pro
SECURITY CODE REVIEW	For critical features	GitHub/GitLab code review, security checklists
COMPLIANCE SCANNING	Continuous	AWS Config, Azure Policy, Cloud Custodian

11.3. Compliance Verification

Multi-standard compliance (OWASP ASVS, NIST SP 800-53, ISO 27001) will be verified through automated tools and manual checks against regulatory requirements such as GDPR, CCPA, and PCI-DSS. Audit preparation will involve ensuring documentation and evidence collection for external audits. Recommendations will include engaging third-party auditors for comprehensive evaluations, ensuring continuous alignment with regulatory standards and best practices for data protection and security.

11.4. Continuous Monitoring

Implement Security Information and Event Management (SIEM) for real-time monitoring, supported by Intrusion Detection/Prevention Systems (IDS/IPS) to identify and mitigate threats. All logs will be aggregated and analyzed for anomalies, facilitating rapid incident detection and response. Continuous monitoring will include behavior analytics to identify potential misuse or security breaches, with integration into incident response processes to ensure prompt action against security events.

11.5. Key Performance Indicators (KPIs)

Mean time to detect (MTTD) security issues
Mean time to remediate (MTTR) vulnerabilities
Percentage of critical vulnerabilities patched within SLA
Security test coverage percentage
False positive rate in automated scanning
Compliance audit pass rate

12. Validation Report

This section presents a comprehensive validation of the security requirements generated throughout this analysis. The validation evaluates the requirements against five key dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. This assessment ensures that the security requirements are comprehensive, technically sound, and actionable for implementation teams.

12.1. Overall Assessment

The overall validation score reflects the quality and completeness of the security requirements across five critical dimensions. Each dimension is scored from 0.0 to 1.0, with 1.0 representing excellent coverage and 0.0 indicating significant gaps.

Overall Score: 0.92/1.0

Validation Status: ✅ PASSED

The security requirements have met the quality threshold (≥0.8) and are ready for implementation. The requirements demonstrate comprehensive coverage, technical accuracy, and alignment with business objectives.

The validation assesses:

Completeness: Are all identified security concerns adequately addressed?
Consistency: Do requirements align with each other without contradictions?
Correctness: Are controls appropriate for the identified risks and correctly applied?
Implementability: Are requirements specific, actionable, and feasible to implement?
Alignment: Do security requirements align with business requirements and objectives?

12.2. Dimension Scores

Dimension	Score	Status
Completeness	0.92	✅
Consistency	0.98	✅
Correctness	0.90	✅
Implementability	0.86	✅
Alignment	0.95	✅

Score Interpretation: - ✅ 0.8-1.0: Excellent - ⚠️ 0.7-0.79: Acceptable (minor improvements needed) - ❌ <0.7: Needs significant improvement

12.3. Detailed Feedback

The provided security requirements and controls are thorough and map well to the original business requirements. Strengths: repository integration/OAuth, RBAC, encryption/KMS, logging/auditability, dependency and secret scanning, CI/CD integration, model lifecycle controls, compliance mappings (GDPR/CCPA/HIPAA/PCI/etc.), and AI/ML high-level threats and monitoring are all addressed with relevant standards and verification guidance. Actionable improvements (prioritized):

Make AI-specific mitigations more concrete and testable
- Define specific prompt-injection mitigations: use deterministic prompt templates, explicit context length limits, prompt sanitizers, allow/deny token lists, and input provenance tagging. Add unit/integration tests that inject malicious payloads to verify sanitizer and template behavior.
- Output redaction/filtering: specify which PII/secret patterns must be redacted, implement field-level redaction rules and a verification test suite that seeds PII and secrets to confirm removal before UI/API exposure.
- Differential privacy & DP parameters: if training on customer code, specify DP algorithms and epsilon bounds or other privacy guarantees; add acceptance tests that measure privacy leakage risk.
- Model poisoning/malicious training data controls: require signed/traced training data, dataset provenance metadata, ingestion whitelists, anomaly detection on training-set statistics, and held-out test failure thresholds to gate model promotion.
Expand observability and automated detection for AI components
- Define concrete metrics & alert thresholds (e.g., sudden change in suggestion rejection rate > X% in 24h, spike in identical outputs, drift in F1/precision by Y). Integrate these into SIEM runbooks with response actions and automated rollback gates.
- Add detection tests for adversarial inputs and continuous adversarial evaluation in CI for model updates.
Harden model inference runtime and data flow
- Specify runtime isolation: container/microVM per tenant or strict multi-tenancy isolation; hardware enclave or HSM use for model keys if required by data residency.
- Define network egress policies for inference VPCs and enforce allowlists to prevent exfiltration.
- Mandatory request/response size limits, rate limits per tenant and per API key with precise quotas (e.g., 60 requests/min/user) and documented throttle behavior.
Make requirements more implementable by adding quantitative acceptance criteria
- For encryption: state accepted algorithms and key rotation cadence (e.g., AES-256 at rest, TLS1.2+; KMS keys rotated every 90 days).
- For logging/audit: retention periods per regulation (e.g., 1 year for general logs, 7 years for SOX evidence) and guarantee of append-only or cryptographic signing for log integrity.
- For caching: define TTLs per artifact class and invalidation triggers (e.g., cache invalidate on commit/PR within 30s) and stress test objectives.
- For access control: define role matrix and SSO protocols supported (OAuth2, SAML 2.0, OIDC), and deprovisioning target SLA (e.g., within 15 minutes of account removal).
Fill missing or underspecified areas
- Secrets handling: require integration with a vault (e.g., HashiCorp Vault or cloud KMS secret manager), ephemeral credential issuance patterns, and mandatory secret rotation workflows for exposed secrets.
- Model supply chain: require cryptographic signatures for externally sourced models, SBOM-like metadata for models, and integrity verification checks during model import.
- Disaster recovery / business continuity: add RPO/RTO targets for critical services (inference API, scanning pipelines, dashboard) and periodic DR testing.
- Data retention and deletion: enumerate retention periods and secure deletion mechanisms per data class; include proof of deletion for customer opt-out requests.
Improve testability and traceability
- Provide a traceability matrix mapping each high-level requirement to specific controls, ownership, and test cases (unit/integration/pen-test requirements).
- Define a baseline threat model with prioritized attack trees per component and measurable mitigation status (e.g., PROMPT-INJ: mitigated/partially mitigated/unmitigated).
Compliance and consent operationalization
- Operationalize consent: define data model for consent metadata, APIs to capture/revoke consent, and enforcement in the training pipeline that rejects data without opt-in. Provide audit logs proving enforcement.
- Map data residency controls to deployment automation: require tagging, enforcement policies, and automated verification tests that assert no cross-border data transfer.

Suggested immediate next steps - Add explicit acceptance criteria for each high-level requirement (security user stories with tests and SLOs).
- Extend AI/ML controls to include concrete metrics, DP parameterization, signed dataset provenance, runtime isolation, and adversarial testing procedures.
- Produce a requirements-to-test matrix and update the control mappings with precise numeric thresholds, tools, and owners.

With these targeted clarifications and quantitative gates, the requirements will be fully implementable, testable, and resilient to AI-specific and platform threats.

Appendix A: Original Requirements Document

AI-Powered Code Review and Security Analysis Platform Requirements

We need to build an AI-powered platform that automatically reviews code commits, identifies security vulnerabilities, and provides intelligent remediation suggestions for development teams.

Key Features:
1. Repository Integration

Connect to GitHub, GitLab, and Bitbucket repositories
OAuth-based authentication for repository access
Webhook integration for real-time commit monitoring
Support for multiple programming languages (Python, Java, JavaScript, Go, etc.)

2. AI-Powered Analysis Engine

LLM-based code review using fine-tuned models (GPT-4, CodeLlama, or custom models)
Static code analysis for vulnerability detection
Pattern recognition for common security anti-patterns
Dependency scanning for known CVEs
Secret detection in code and configuration files

3. Intelligent Remediation

AI-generated fix suggestions with code snippets
Automatic pull request creation with security fixes
Severity scoring and risk assessment
Context-aware explanations of vulnerabilities

4. Team Collaboration

Developer dashboards with personalized security metrics
Comment threads on identified issues
Integration with existing CI/CD pipelines
Slack/Teams notifications for critical findings

5. Compliance and Reporting

OWASP Top 10 compliance checking
Custom security policy enforcement
Audit logs of all security findings
Export reports for compliance documentation
Trend analysis of security posture over time

6. Model Management

API endpoints for model inference
Model versioning and A/B testing
Custom model training on organization's codebase
Model performance monitoring and drift detection

7. Data Handling

Process and store code snippets for analysis
Maintain vulnerability database
Store remediation history and patterns
Cache analysis results for performance

The platform will handle sensitive source code, authentication tokens, API keys, and potentially expose AI models through APIs. It requires integration with various third-party services, processes potentially proprietary code, and needs to maintain strict access controls while providing actionable security insights.

Appendix B: Glossary

Term	Definition
ASVS	Application Security Verification Standard (OWASP)
STRIDE	Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege
SAST	Static Application Security Testing
DAST	Dynamic Application Security Testing
MFA	Multi-Factor Authentication
RBAC	Role-Based Access Control
PII	Personally Identifiable Information
PHI	Protected Health Information
GDPR	General Data Protection Regulation
HIPAA	Health Insurance Portability and Accountability Act
PCI-DSS	Payment Card Industry Data Security Standard

Appendix C: Complete Threat List

This appendix contains the complete list of all identified threats with full descriptions and mitigation strategies. Threats are organized by risk level for easy reference.

Critical Risk Threats

THR-001 - Edge/API Gateway

Category: Spoofing
Likelihood: High | Impact: High
Risk Level: Critical
Description: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsigned/weakly-signed tokens) allowing an attacker to impersonate users or service accounts and access repositories, create scans, or view results.
Mitigation Strategy: Enforce strict OAuth flows and short-lived tokens, validate JWT signatures and claims (iss/aud/exp), use proof-of-possession or mTLS for high-priv tokens, rotate tokens frequently, require token binding for webhooks, monitor for suspicious token usage, rate-limit token usage, and implement device/session fingerprinting. Enforce scope minimization on OAuth clients.

THR-009 - AI Analysis Engine

Category: Information Disclosure
Likelihood: High | Impact: High
Risk Level: Critical
Description: Sensitive source code or secrets are transmitted to or stored on external managed model hosts (SaaS model providers) where tenant separation, contractual protections, or inadvertent logging could lead to leakage of proprietary code or credentials.
Mitigation Strategy: Offer private/on-prem model deployment options, require E2E encryption (client to model), ensure model hosts provide data isolation contracts, avoid logging raw code snippets at the model host, minimize data sent to external providers (use extracted features), use BYOK and VPC peering for private deployments, and provide explicit opt-in for sending full code to managed hosts.

High Risk Threats

THR-002 - Edge/API Gateway

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Manipulation of inbound API requests or webhook payloads en route to backend (e.g., altering scan parameters, redirecting callbacks, or injecting malicious fields) to change scan behavior or escalate access.
Mitigation Strategy: Validate webhook signatures (HMAC), verify sender IPs where feasible, use mutual authentication (mTLS) for integrations, perform strict input validation and schema checks at gateway, sign and/or timestamp important parameters, and record an immutable request audit trail.

THR-003 - Edge/API Gateway

Category: Denial of Service
Likelihood: Medium | Impact: High
Risk Level: High
Description: API gateway or edge infrastructure overwhelmed by high request volume or crafted requests (flooding, slowloris, large payloads) causing availability loss for scanning and developer access.
Mitigation Strategy: Implement rate limiting, request throttling, bot detection, WAF and DDoS protection, circuit breakers, autoscaling with quotas, request size limits, and progressive backoff for heavy analysis requests. Use API keys and quotas per tenant.

THR-004 - Frontend Layer

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, findings or tokens to other users or remote hosts; stored XSS in comment threads revealing sensitive diffs.
Mitigation Strategy: Apply strict output encoding/escaping, implement a strong Content Security Policy (CSP), sanitize user inputs, adopt secure templating frameworks, use HttpOnly and Secure cookies, enforce same-site cookie attributes, and perform regular frontend security testing.

THR-006 - Application Services

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Forged repository webhook events or API calls cause the application to perform unauthorized actions such as merging PRs, applying fixes, or changing RBAC settings.
Mitigation Strategy: Verify webhook signatures, use per-integration secrets, enforce least privilege for integration accounts, require branch protection and signed commits for automatic merges, and add manual approvals for high-impact automated fixes.

THR-008 - Application Services

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Broken access control leading to unauthorized users (or tenants) reading repository metadata, scan results, vulnerability details, or remediation suggestions.
Mitigation Strategy: Enforce RBAC/ABAC with least privilege, implement strong multi-tenant isolation, perform authorization checks server-side for each resource, adopt just-in-time access, and provide fine-grained data redaction/scoping.

THR-010 - AI Analysis Engine

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Poisoning of training data or prompt injection via crafted code causing models to generate insecure or malicious remediation suggestions (e.g., suggesting credential exfiltration or insecure configurations).
Mitigation Strategy: Validate and sanitize training data, maintain provenance of training artifacts, isolate customer training sets, perform adversarial testing and red-team model evaluation, implement prompt and output filtering, and require deterministic SAST cross-checks for suggested fixes.

THR-011 - AI Analysis Engine

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Risk Level: High
Description: LLM-generated remediation automatically introduces code that elevates privileges (e.g., creating backdoor admin endpoints or insecurely setting permissions) and is merged without adequate review.
Mitigation Strategy: Require human review or automated safety checks for high-risk code changes, implement automated SAST/IAST validation of suggested patches, prevent automatic merges for critical severity changes, and enforce branch protections and signed approvals for elevated-permission changes.

THR-012 - AI Analysis Engine

Category: Denial of Service
Likelihood: High | Impact: Medium
Risk Level: High
Description: Excessive inference requests (automated or malicious) saturate model hosting or GPU capacity, causing analysis delays or denial of analysis service to other tenants.
Mitigation Strategy: Implement per-tenant quotas, rate-limiting, queueing/backpressure, autoscaling of inference clusters, graceful degradation to lightweight scanners, and cost-control safeguards. Monitor usage and detect anomalous patterns.

THR-014 - Secret Detection / Data Storage

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Secrets (API keys, tokens, private keys) stored in persistent storage, caches, backups, logs, or remediation history in plaintext or weakly encrypted form leading to leak and unauthorized access to third-party systems.
Mitigation Strategy: Encrypt secrets at rest using KMS/BYOK, use envelope encryption, redact secrets from UI/logs, avoid storing full secrets (store hashed/hashed pointers), rotate secrets on detection, implement strict access controls and auditing for key material.

THR-015 - Model Management

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Unauthorized modification or replacement of model binaries or metadata (model poisoning or substitution) causing compromised inference results across tenants.
Mitigation Strategy: Secure model registries with RBAC, sign model artifacts, use integrity checks (hashes/signatures), store models in hardened immutable storage, require approvals for model changes, and log/model change audit trails.

THR-016 - Model Management

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Leakage of model binaries or customer-specific training data/artifacts used for fine-tuning (e.g., code used in training accessible to other customers or leaked externally).
Mitigation Strategy: Isolate customer training environments (VPC/on-prem), encrypt training datasets and artifacts, use access controls and logging for training storage, avoid multi-tenant mixing of data during training, and provide contractual controls for managed hosts.

THR-017 - Data Storage & Persistence

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Public exposure of backups, snapshots, or misconfigured storage buckets containing code artifacts, vulnerability DB, or audit logs (e.g., publicly accessible S3 bucket).
Mitigation Strategy: Enforce least-privilege IAM for storage, block public access by default, encrypt backups with KMS/BYOK, perform automated scans for open storage, enforce MFA and approval for snapshot exports, and implement immutable retention where required.

THR-018 - Data Storage & Persistence

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Unauthorized modification or deletion of audit logs, remediation history, or vulnerability records to erase evidence or alter findings.
Mitigation Strategy: Store critical logs in WORM/append-only storage, forward copies to a hardened SIEM, apply strict IAM and separation of duties for log access, cryptographically sign logs, and enable integrity verification.

THR-019 - External Integrations

Category: Spoofing
Likelihood: Medium | Impact: High
Risk Level: High
Description: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed events or uses stolen integration credentials to manipulate the platform (trigger scans, inject malicious webhooks).
Mitigation Strategy: Enforce webhook signature verification, use short-lived integration credentials, require mutual TLS or IP allowlisting where possible, restrict delegated permissions, and monitor integration behavior for anomalies.

THR-020 - External Integrations

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Integration tokens, API keys, or credentials logged in plain-text (application logs, access logs, stack traces) or included in notifications (Slack/Teams) leading to compromise of third-party services.
Mitigation Strategy: Redact secrets from logs and notifications, store tokens encrypted (KMS), rotate integration tokens frequently, implement scoped tokens with minimal privileges, and apply strict logging policies to avoid sensitive data in outputs.

THR-022 - Repository Integration (OAuth)

Category: Spoofing
Likelihood: Medium | Impact: High
Risk Level: High
Description: OAuth application granted excessive scopes or abused consent to gain broad repository access; attacker leverages app to exfiltrate code or create/merge PRs.
Mitigation Strategy: Require least-privilege scopes for OAuth apps, implement granular scope review and just-in-time elevation for higher scopes, show clear consent screens, use fine-grained tokens, and implement periodic automatic token and scope reviews.

THR-023 - CI/CD Integrations

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Risk Level: High
Description: Malicious pipeline step or compromised CI runner introduced via a PR allows execution of malicious code with elevated privileges (e.g., pushing to production or accessing secrets), especially when automatic merges are permitted.
Mitigation Strategy: Enforce pipeline protection (trusted runners), require CI job signatures or provenance, restrict secrets to ephemeral tokens injected only at runtime, require code reviews for pipeline changes, and avoid automatic merge-to-main without approvals.

THR-026 - Data Handling / Caching

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Cache poisoning or tenant key collisions cause cached analysis results, diffs, or findings to be returned to the wrong tenant or user, exposing proprietary code or vulnerability data.
Mitigation Strategy: Enforce tenant-scoped cache keys, encrypt cache values where sensitive, set conservative TTLs for cached code/artifacts, isolate caches per tenant when feasible, and validate cache hit tenant context before returning results.

THR-027 - Application Services / APIs

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Classic injection attacks (SQL injection, command injection) or unsafe deserialization in API endpoints exposing back-end data stores or enabling remote code execution within application services.
Mitigation Strategy: Use parameterized queries/ORMs, strict input validation and allow-lists, safe deserialization libraries, WAF protections, regular dependency vulnerability management, and secure coding practices plus SAST/DAST testing in CI.

THR-028 - Edge/API Gateway

Category: Elevation of Privilege
Likelihood: Low | Impact: High
Risk Level: High
Description: Misconfiguration in gateway RBAC or routing allows privilege escalation to administrative APIs (e.g., a normal user can access management endpoints or modify ACLs).
Mitigation Strategy: Harden gateway admin endpoints behind separate network controls and MFA, apply strict RBAC/ABAC for management APIs, separate admin plane from data plane, perform regular configuration audits, and use automated policy-as-code tests on deployments.

Medium Risk Threats

THR-005 - Frontend Layer

Category: Spoofing
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating PRs with suggested fixes) performed by authenticated users without their knowledge.
Mitigation Strategy: Enforce anti-CSRF tokens or same-site cookies for state-changing operations, require re-authentication/MFA for critical actions, validate Origin/Referer, and use strict CORS policies.

THR-007 - Application Services

Category: Repudiation
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who initiated a scan, who created/approved a PR), complicating incident response and compliance.
Mitigation Strategy: Generate immutable, tamper-evident audit logs with strong timestamps, forward logs to a hardened SIEM, implement write-once storage for critical logs, cryptographically sign important events, and implement log retention and access controls.

THR-013 - AI Analysis Engine / Vulnerability Feeds

Category: Information Disclosure
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability metadata causing false positives/negatives or incorrect remediation advice.
Mitigation Strategy: Use signed/verifiable CVE feeds, cross-validate vulnerabilities from multiple trusted sources, implement caching with integrity verification, and maintain an internal vulnerability DB with human review for high-impact items.

THR-021 - External Integrations

Category: Denial of Service
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Flooding of webhook endpoints or continuous integration triggers to consume resources (e.g., force repeated scanning or push heavy workloads) causing degraded service.
Mitigation Strategy: Implement webhook rate limiting, signature verification, debounce/aggregation for repeated events, and require challenge-response during initial webhook registration. Provide per-tenant quotas.

THR-024 - Compliance and Reporting

Category: Repudiation
Likelihood: Low | Impact: Medium
Risk Level: Medium
Description: Tampering with compliance reports or trend analysis dashboards (intentional or accidental) to hide vulnerabilities, creating false compliance posture.
Mitigation Strategy: Cryptographically sign and timestamp exported reports, maintain immutable archived copies for audits, enforce access controls for report generation and modification, and monitor report generation and download events.

THR-025 - Team Collaboration

Category: Information Disclosure
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Comment threads or dashboards inadvertently exposing sensitive code, PII, or secrets to unauthorized team members or external collaborators.
Mitigation Strategy: Enforce RBAC for comment visibility, auto-redact detected secrets and sensitive code snippets in comments, allow configurable visibility (private/public), and provide data minimization by default.

Total Threats: 28

Appendix D: Complete Requirements Traceability Matrix

This appendix provides complete end-to-end traceability from requirements through threats to controls and verification.

Full Traceability Table

Req ID	Requirement	Category	Sensitivity	Threat IDs	Security Controls	Priority	Verification	Status
REQ-001	Repository integration with GitHub, GitLab, and Bi…	Repository Integration / Authentication	High	THR-002, THR-003, THR-005 +7	[OWASP] ASVS 2.3.1, [NIST] AC-3, [ISO27001] A.9.4.2	Critical	Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens., Inspect access control logic; run tests that attempt unauthorized repo access; review logs confirming enforcement of authorization decisions.	Pending
REQ-002	Support multi-language parsing and analysis for Py…	Analysis Engine	Medium	THR-009, THR-010, THR-011 +6	[OWASP] ASVS 8.1.1, [NIST] SA-11, [OWASP] ASVS 14.1.1	High	Review threat models and rulesets for each supported language; test scanners against seeded test cases covering language-specific issues., Audit sampled findings, review false-positive rates per language, and confirm manual review steps exist in process documentation.	Pending
REQ-003	Provide an LLM-based code review engine exposed vi…	Model Management / Inference	High	THR-001, THR-002, THR-003 +7	[NIST] SA-15, [ISO27001] A.12.5.1, [OWASP] ASVS 12.1.1	Critical	Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers., Review model versioning logs, CI/CD pipelines for model deployment, and security gating on model promotion to production.	Pending
REQ-004	Perform static code analysis (SAST) and pattern re…	Analysis Engine / Security Testing	High	THR-004, THR-005, THR-006 +7	[OWASP] ASVS 14.2.1, [NIST] RA-5	Critical	Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates., Verify scheduled scans run; check vulnerability tickets and remediation timelines; validate coverage of scanning tools.	Pending
REQ-005	Dependency scanning that identifies libraries, ver…	Dependency Management / Vulnerability Management	High	THR-003, THR-005, THR-008 +6	[NIST] SA-12, [OWASP] ASVS 14.4.1, [ISO27001] A.15.1.1	Critical	Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI., Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans.	Pending
REQ-006	Secret detection across code, config files, and co…	Secret Management / Data Protection	High	THR-003, THR-005, THR-006 +7	[NIST] SA-12, [OWASP] ASVS 14.4.1, [ISO27001] A.15.1.1	Critical	Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI., Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans.	Pending
REQ-007	Generate AI remediation suggestions with code snip…	Remediation / Developer Productivity	Medium	THR-004, THR-006, THR-008 +7	[NIST] PL-4, [OWASP] ASVS 12.1.1, [ISO27001] A.14.2.3	High	Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions., Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage.	Pending
REQ-008	Support automatic pull request creation with propo…	Automation / CI/CD Integration	High	THR-002, THR-003, THR-005 +4	[NIST] CM-3, [OWASP] ASVS 14.1.1, [ISO27001] A.12.1.2	Critical	Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs., Verify workflow enforces approvals, review automated PRs created and ensure a rollback path exists with audit logs.	Pending
REQ-009	Provide severity scoring and risk assessment that …	Risk Management	Medium	THR-004, THR-009, THR-010 +7	[NIST] RA-3, [OWASP] ASVS 14.4.1, [ISO27001] A.8.2.1	Critical	Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans., Check classification registers and verify scoring integrates classification attributes into prioritization.	Pending
REQ-010	Context-aware explanations for each finding, inclu…	Reporting / Developer Support	Medium	THR-004, THR-009, THR-010 +7	[NIST] PL-4, [OWASP] ASVS 12.1.1, [ISO27001] A.14.2.3	High	Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions., Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage.	Pending
REQ-011	Developer dashboards displaying personalized secur…	Team Collaboration / Analytics	Medium	THR-003, THR-009, THR-010 +6	[OWASP] ASVS 12.1.1, [NIST] AU-6, [ISO27001] A.7.2.2	High	Test API endpoints for access control bypass, review role mappings, and inspect UI rendering for sensitive data leaks., Verify links to training are available and that metrics are used in awareness programs.	Pending
REQ-012	Support comment threads on identified issues, assi…	Collaboration / Workflow	Medium	THR-001, THR-004, THR-011 +7	None	Medium	Manual Review	Pending
REQ-013	Integrate with CI/CD pipelines (e.g., GitHub Actio…	CI/CD Integration	High	THR-001, THR-002, THR-004 +7	[OWASP] ASVS 14.1.1, [NIST] SA-11, [ISO27001] A.14.2.1	Critical	Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement., Review CI pipeline configs, confirm scans run on pre-merge and post-merge events, and validate PR blocking behavior for critical issues.	Pending
REQ-014	Send configurable notifications for critical findi…	Notifications / Collaboration	Medium	THR-004, THR-010, THR-011 +5	None	Medium	Manual Review	Pending
REQ-015	Provide OWASP Top 10 checks and allow creation/enf…	Compliance / Policy Enforcement	Medium	THR-001, THR-009, THR-016 +4	[OWASP] ASVS 14.4.1, [NIST] PL-2, [ISO27001] A.5.1.1	Critical	Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated., Test custom rule creation, verify policy mapping to OWASP categories, and confirm enforcement in scans and gates.	Pending
REQ-016	Maintain immutable audit logs of all analyses, fin…	Audit / Compliance	High	THR-001, THR-002, THR-003 +7	[NIST] AU-2, [OWASP] ASVS 12.3.1, [NIST] AU-9	Critical	Review audit event definitions, sample logs for required events, and validate retention and integrity protections., Verify cryptographic log integrity, attempt tamper tests in controlled env, and review log access controls.	Pending
REQ-017	Exportable compliance reports and evidence package…	Reporting / Compliance	Medium	THR-007, THR-008, THR-010 +7	[ISO27001] A.18.2.3, [NIST] CA-7, [OWASP] ASVS 12.1.1	High	Test export APIs for access control enforcement and verify signatures on exported packages., Validate that continuous monitoring feeds into report generation and that exported reports contain verifiable signatures.	Pending
REQ-018	Model management features including model versioni…	Model Management / Governance	High	THR-009, THR-010, THR-012 +7	None	Medium	Manual Review	Pending
REQ-019	Enable custom model training on customers’ codebas…	Model Training / Data Privacy	High	THR-002, THR-005, THR-007 +7	[NIST] PL-4, [ISO27001] A.8.2.3, [OWASP] ASVS 14.3.1	Critical	Inspect rules of behavior, check anonymization pipelines, and confirm consent records for training datasets., Run secret scans on training datasets, validate redaction effectiveness, and review provenance logs.	Pending
REQ-020	Model performance monitoring and drift detection w…	Model Monitoring / Quality	Medium	THR-009, THR-010, THR-012 +7	[NIST] SA-15, [OWASP] ASVS 12.1.1, [NIST] SI-4	Critical	Review governance artifacts, model registries, A/B testing configs, and access controls for model artifacts., Verify authN/Z on endpoints, confirm version headers are present in logs, and review telemetry ingestion.	Pending
REQ-021	Secure processing and storage of code snippets, an…	Data Handling / Security	High	THR-001, THR-002, THR-003 +7	[NIST] SC-13, [ISO27001] A.10.1.1, [OWASP] ASVS 2.4.1	Critical	Inspect encryption settings, redaction logic, and confirm secrets are stored only in approved vaults with access auditing., Review cryptography policy and sample stored artifacts for correct encryption and redaction settings.	Pending
REQ-022	Maintain and update a vulnerability database mappe…	Vulnerability Management / Threat Intelligence	Medium	THR-008, THR-013, THR-014 +4	[NIST] CM-8, [OWASP] ASVS 14.4.1, [ISO27001] A.12.6.1	Critical	Examine vulnerability management reports, SLA adherence, and evidence of action on identified CVEs., Review feed ingestion logs, confirm CVE-to-component mappings, and check remediation ticket lifecycle.	Pending
REQ-023	Cache analysis results for performance with secure…	Performance / Data Management	Medium	THR-001, THR-004, THR-005 +7	[OWASP] ASVS 14.3.1, [NIST] SC-12, [ISO27001] A.10.1.1	Critical	Inspect KM systems, verify rotation and revocation procedures were executed for exposed secrets, and review access controls to secrets., Run secret scanning on historical repositories, validate detection of seeded secrets, and confirm blocking policy enforcement.	Pending
REQ-024	Implement fine-grained access control and RBAC for…	Authentication / Authorization	High	THR-001, THR-002, THR-003 +7	[NIST] AC-2, [OWASP] ASVS 2.2.1, [ISO27001] A.9.1.2	Critical	Test role-based access enforcement for UI and APIs and verify session handling behaviors., Review account provisioning logs, role assignments and deprovision records; perform access reviews.	Pending
REQ-025	API gateway protections: API key management, OAuth…	API Security / Operational Security	High	THR-001, THR-002, THR-003 +7	[OWASP] ASVS 12.1.1, [NIST] IA-5, [ISO27001] A.13.1.1	Critical	Review authenticator policies, test brute-force protections and check account lockout logs., Review edge proxy configs, WAF rules, and DDoS protection telemetry during simulated abuse.	Pending
REQ-026	Support deployment options: SaaS with VPC peering,…	Deployment / Infrastructure	High	THR-008, THR-009, THR-010 +7	[OWASP] ASVS 14.1.1, [NIST] SC-7, [ISO27001] A.8.1.3	High	Review deployment manifests for isolation features and validate data residency enforcement in sample deployments., Pen-test network boundaries, inspect VPC configs, and verify management plane access restrictions.	Pending
REQ-027	Data usage and consent controls including the abil…	Privacy / Data Governance	High	THR-003, THR-004, THR-008 +7	[ISO27001] A.18.1.4, [NIST] PL-4, [OWASP] ASVS 2.4.1	Critical	Check rules of behavior documentation and verify system honors stored user preferences during training runs., Inspect dataset tagging and training pipelines, confirm that opt-out tags prevent inclusion in training datasets.	Pending
REQ-028	Integrate platform monitoring with SIEM/SOAR for s…	Monitoring / Incident Response	High	THR-007, THR-009, THR-014 +1	[NIST] SI-4, [ISO27001] A.16.1.5, [OWASP] ASVS 12.3.1	Critical	Review incident response plans, confirm SOPs for SIEM alerts, and evaluate results of tabletop or live exercises., Audit logs for required context fields, verify SIEM correlation rules and test full incident response flow.	Pending

Total Requirements Tracked: 28

Detailed Requirement Mappings

The following section provides detailed traceability for each requirement:

REQ-001: Repository integration with GitHub, GitLab, and Bitbucket using OAuth-based authentication, webhook …

Related Threats:

THR-002: Manipulation of inbound API requests or webhook payloads en route to backend (e….
THR-003: API gateway or edge infrastructure overwhelmed by high request volume or crafted…
THR-005: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P…
THR-006: Forged repository webhook events or API calls cause the application to perform u…
THR-008: Broken access control leading to unauthorized users (or tenants) reading reposit…
…and 5 more threats

Security Controls:

[OWASP] ASVS 2.3.1: [OWASP] Ensure secure single sign-on, federated authentication and OAuth integration fol…
[NIST] AC-3: [NIST] The information system enforces approved authorizations for controlling access t…
[ISO27001] A.9.4.2: [ISO27001] Where required by the access control policy, secure log-on procedures shall be i…

Verification: Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens., Inspect access control logic; run tests that attempt unauthorized repo access; review logs confirming enforcement of authorization decisions., Review OAuth implementation code/config; inspect scope requested during OAuth flows; verify webhook signature verification and token storage protections during pen test and code review.

Priority: Critical | Status: Pending

REQ-002: Support multi-language parsing and analysis for Python, Java, JavaScript/TypeScript, Go, and an exte…

Related Threats:

THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
THR-012: Excessive inference requests (automated or malicious) saturate model hosting or …
THR-013: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability …
…and 4 more threats

Security Controls:

[OWASP] ASVS 8.1.1: [OWASP] Perform architecture and design level analysis and include language-specific sec…
[NIST] SA-11: [NIST] The organization requires developers to employ security testing tools and techni…
[OWASP] ASVS 14.1.1: [OWASP] Source code should be reviewed by automated tools and manually to detect flaws a…

Verification: Review threat models and rulesets for each supported language; test scanners against seeded test cases covering language-specific issues., Audit sampled findings, review false-positive rates per language, and confirm manual review steps exist in process documentation., Verify tool inventory and configurations include language-specific scanners; run comparative analysis with benchmark projects.

Priority: High | Status: Pending

REQ-003: Provide an LLM-based code review engine exposed via secure API endpoints for inference, supporting f…

Related Threats:

THR-001: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi…
THR-002: Manipulation of inbound API requests or webhook payloads en route to backend (e….
THR-003: API gateway or edge infrastructure overwhelmed by high request volume or crafted…
THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-005: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P…
…and 5 more threats

Security Controls:

[NIST] SA-15: [NIST] The organization requires that development processes and tools support secure de…
[ISO27001] A.12.5.1: [ISO27001] Changes to organization assets and services should be controlled and managed inc…
[OWASP] ASVS 12.1.1: [OWASP] Ensure secure design and versioning for APIs and that API endpoints enforce auth…

Verification: Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers., Review model versioning logs, CI/CD pipelines for model deployment, and security gating on model promotion to production., Inspect change tickets for model deployments, verify rollback procedures, and confirm version metadata is immutable and auditable.

Priority: Critical | Status: Pending

REQ-004: Perform static code analysis (SAST) and pattern recognition to detect insecure coding patterns, misc…

Related Threats:

THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-005: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P…
THR-006: Forged repository webhook events or API calls cause the application to perform u…
THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
…and 5 more threats

Security Controls:

[OWASP] ASVS 14.2.1: [OWASP] Use automated static analysis tools to detect common security weaknesses and ant…
[NIST] RA-5: [NIST] The organization scans for vulnerabilities and identifies vulnerabilities in sys…

Verification: Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates., Verify scheduled scans run; check vulnerability tickets and remediation timelines; validate coverage of scanning tools.

Priority: Critical | Status: Pending

REQ-005: Dependency scanning that identifies libraries, versions, and maps to known CVEs (NVD/CVE feeds), wit…

Related Threats:

THR-003: API gateway or edge infrastructure overwhelmed by high request volume or crafted…
THR-005: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P…
THR-008: Broken access control leading to unauthorized users (or tenants) reading reposit…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
…and 4 more threats

Security Controls:

[NIST] SA-12: [NIST] The organization manages supply chain risks and implements protections against s…
[OWASP] ASVS 14.4.1: [OWASP] Verify and manage third-party components and dependencies; ensure known vulnerab…
[ISO27001] A.15.1.1: [ISO27001] Organizations should identify and manage security risks associated with supplier…

Verification: Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI., Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans., Audit dependency scan reports, confirm mapping to CVE/NVD, and verify remediation tickets exist for critical findings.

Priority: Critical | Status: Pending

REQ-006: Secret detection across code, config files, and commits using pattern detection, entropy checks, and…

Related Threats:

THR-003: API gateway or edge infrastructure overwhelmed by high request volume or crafted…
THR-005: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P…
THR-006: Forged repository webhook events or API calls cause the application to perform u…
THR-008: Broken access control leading to unauthorized users (or tenants) reading reposit…
THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
…and 5 more threats

Security Controls:

[NIST] SA-12: [NIST] The organization manages supply chain risks and implements protections against s…
[OWASP] ASVS 14.4.1: [OWASP] Verify and manage third-party components and dependencies; ensure known vulnerab…
[ISO27001] A.15.1.1: [ISO27001] Organizations should identify and manage security risks associated with supplier…

Priority: Critical | Status: Pending

REQ-007: Generate AI remediation suggestions with code snippets, unit/regression test suggestions, and contex…

Related Threats:

THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-006: Forged repository webhook events or API calls cause the application to perform u…
THR-008: Broken access control leading to unauthorized users (or tenants) reading reposit…
THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
…and 5 more threats

Security Controls:

[NIST] PL-4: [NIST] Define acceptable use and rules for automated tools and AI, specifying responsib…
[OWASP] ASVS 12.1.1: [OWASP] Ensure APIs delivering code suggestions enforce authentication, authorization, a…
[ISO27001] A.14.2.3: [ISO27001] Protection of data used for testing and development including sanitized code sam…

Verification: Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions., Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage., Pen-test suggestion APIs, inspect redaction logic for outputs, and verify auth and logging configuration.

Priority: High | Status: Pending

REQ-008: Support automatic pull request creation with proposed fixes, configurable auto-apply actions, and an…

Related Threats:

THR-002: Manipulation of inbound API requests or webhook payloads en route to backend (e….
THR-003: API gateway or edge infrastructure overwhelmed by high request volume or crafted…
THR-005: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P…
THR-006: Forged repository webhook events or API calls cause the application to perform u…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
…and 2 more threats

Security Controls:

[NIST] CM-3: [NIST] Changes to system configuration and software must be controlled, including appro…
[OWASP] ASVS 14.1.1: [OWASP] Source code changes should be subject to review and must be tracked by secure pr…
[ISO27001] A.12.1.2: [ISO27001] Changes to information processing facilities and systems should be managed follo…

Verification: Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs., Verify workflow enforces approvals, review automated PRs created and ensure a rollback path exists with audit logs., Review change records for auto-applied fixes, confirm approval traces, and test rollback processes.

Priority: Critical | Status: Pending

REQ-009: Provide severity scoring and risk assessment that combines CVSS, exploitability, code context (usage…

Related Threats:

THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
THR-016: Leakage of model binaries or customer-specific training data/artifacts used for …
…and 5 more threats

Security Controls:

[NIST] RA-3: [NIST] Conduct risk assessments to identify threats and vulnerabilities and determine r…
[OWASP] ASVS 14.4.1: [OWASP] Vulnerabilities should be prioritized by severity and context to guide remediati…
[ISO27001] A.8.2.1: [ISO27001] Information should be classified to support risk-based handling and prioritizati…

Verification: Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans., Check classification registers and verify scoring integrates classification attributes into prioritization., Review risk assessment methodology, sample scored findings and ensure scoring matches defined criteria and business context.

Priority: Critical | Status: Pending

REQ-010: Context-aware explanations for each finding, including code locations, stack traces, reproduction st…

Related Threats:

THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
THR-016: Leakage of model binaries or customer-specific training data/artifacts used for …
…and 5 more threats

Security Controls:

[NIST] PL-4: [NIST] Define acceptable use and rules for automated tools and AI, specifying responsib…
[OWASP] ASVS 12.1.1: [OWASP] Ensure APIs delivering code suggestions enforce authentication, authorization, a…
[ISO27001] A.14.2.3: [ISO27001] Protection of data used for testing and development including sanitized code sam…

Priority: High | Status: Pending

REQ-011: Developer dashboards displaying personalized security metrics, team-level metrics, live issue queues…

Related Threats:

THR-003: API gateway or edge infrastructure overwhelmed by high request volume or crafted…
THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
THR-012: Excessive inference requests (automated or malicious) saturate model hosting or …
…and 4 more threats

Security Controls:

[OWASP] ASVS 12.1.1: [OWASP] APIs providing dashboards must enforce authentication, authorization, and secure…
[NIST] AU-6: [NIST] Review and analyze audit records and report findings to support security monitor…
[ISO27001] A.7.2.2: [ISO27001] Employees should receive appropriate awareness and training to support informati…

Verification: Test API endpoints for access control bypass, review role mappings, and inspect UI rendering for sensitive data leaks., Verify links to training are available and that metrics are used in awareness programs., Compare dashboard metrics against raw audit logs to ensure accuracy and completeness.

Priority: High | Status: Pending

REQ-012: Support comment threads on identified issues, assignment workflows, and integrations that allow deve…

Related Threats:

THR-001: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi…
THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
THR-018: Unauthorized modification or deletion of audit logs, remediation history, or vul…
THR-019: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed …
…and 5 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

REQ-013: Integrate with CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins) to run pre-merge and post-…

Related Threats:

THR-001: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi…
THR-002: Manipulation of inbound API requests or webhook payloads en route to backend (e….
THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-005: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P…
THR-007: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who…
…and 5 more threats

Security Controls:

[OWASP] ASVS 14.1.1: [OWASP] Incorporate automated security testing into the development lifecycle and CI/CD …
[NIST] SA-11: [NIST] Require developer security testing and evaluation, including integration into bu…
[ISO27001] A.14.2.1: [ISO27001] Establish secure development policies including integration of security testing …

Verification: Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement., Review CI pipeline configs, confirm scans run on pre-merge and post-merge events, and validate PR blocking behavior for critical issues., Review secure development policy and pipeline configurations for evidence of enforcement.

Priority: Critical | Status: Pending

REQ-014: Send configurable notifications for critical findings to Slack and Microsoft Teams with secure links…

Related Threats:

THR-004: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
THR-018: Unauthorized modification or deletion of audit logs, remediation history, or vul…
THR-019: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed …
…and 3 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

REQ-015: Provide OWASP Top 10 checks and allow creation/enforcement of custom security policies and rule sets…

Related Threats:

THR-001: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi…
THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-016: Leakage of model binaries or customer-specific training data/artifacts used for …
THR-019: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed …
THR-023: Malicious pipeline step or compromised CI runner introduced via a PR allows exec…
…and 2 more threats

Security Controls:

[OWASP] ASVS 14.4.1: [OWASP] Enforce security policies including OWASP Top 10 and provide mechanisms for cust…
[NIST] PL-2: [NIST] Develop and disseminate system and communications protection policies and proced…
[ISO27001] A.5.1.1: [ISO27001] Management should define a set of information security policies to provide manag…

Verification: Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated., Test custom rule creation, verify policy mapping to OWASP categories, and confirm enforcement in scans and gates., Check for signed policy documents and traceability between policies and enforcement mechanisms.

Priority: Critical | Status: Pending

REQ-016: Maintain immutable audit logs of all analyses, findings, remediation actions, approvals, and access …

Related Threats:

THR-001: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi…
THR-002: Manipulation of inbound API requests or webhook payloads en route to backend (e….
THR-003: API gateway or edge infrastructure overwhelmed by high request volume or crafted…
THR-007: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who…
THR-008: Broken access control leading to unauthorized users (or tenants) reading reposit…
…and 5 more threats

Security Controls:

[NIST] AU-2: [NIST] The information system provides audit records for the organization-defined event…
[OWASP] ASVS 12.3.1: [OWASP] Ensure that security-relevant events are logged with integrity and protected fro…
[NIST] AU-9: [NIST] Protect audit records from unauthorized access, modification, and deletion to pr…

Verification: Review audit event definitions, sample logs for required events, and validate retention and integrity protections., Verify cryptographic log integrity, attempt tamper tests in controlled env, and review log access controls., Review access lists for log storage, check encryption keys usage, and validate log retention policies.

Priority: Critical | Status: Pending

REQ-017: Exportable compliance reports and evidence packages (PDF/CSV/JSON) covering findings, remediation hi…

Related Threats:

THR-007: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who…
THR-008: Broken access control leading to unauthorized users (or tenants) reading reposit…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-011: LLM-generated remediation automatically introduces code that elevates privileges…
THR-013: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability …
…and 5 more threats

Security Controls:

[ISO27001] A.18.2.3: [ISO27001] Information security should be reviewed regularly and results available to demon…
[NIST] CA-7: [NIST] Establish continuous monitoring processes to produce evidence and reports of sys…
[OWASP] ASVS 12.1.1: [OWASP] APIs that export reports must enforce access controls and ensure integrity of ex…

Verification: Test export APIs for access control enforcement and verify signatures on exported packages., Validate that continuous monitoring feeds into report generation and that exported reports contain verifiable signatures., Produce sample evidence packages for audits and confirm completeness against compliance checklists.

Priority: High | Status: Pending

REQ-018: Model management features including model versioning, A/B testing frameworks, staged rollouts, and l…

Related Threats:

THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-012: Excessive inference requests (automated or malicious) saturate model hosting or …
THR-014: Secrets (API keys, tokens, private keys) stored in persistent storage, caches, b…
THR-015: Unauthorized modification or replacement of model binaries or metadata (model po…
…and 5 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

REQ-020: Model performance monitoring and drift detection with alerts, metrics (precision/recall/false positi…

Related Threats:

THR-009: Sensitive source code or secrets are transmitted to or stored on external manage…
THR-010: Poisoning of training data or prompt injection via crafted code causing models t…
THR-012: Excessive inference requests (automated or malicious) saturate model hosting or …
THR-013: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability …
THR-014: Secrets (API keys, tokens, private keys) stored in persistent storage, caches, b…
…and 5 more threats

Security Controls:

[NIST] SA-15: [NIST] The organization ensures development processes and tools support secure design, …
[OWASP] ASVS 12.1.1: [OWASP] Ensure API endpoints enforce authentication, authorization, and logging to suppo…
[NIST] SI-4: [NIST] Monitor the information system to detect attacks and potential misuse, including…

Verification: Review governance artifacts, model registries, A/B testing configs, and access controls for model artifacts., Verify authN/Z on endpoints, confirm version headers are present in logs, and review telemetry ingestion., Review monitoring rules, simulate drift scenarios, and confirm alerts trigger defined responses.

Priority: Critical | Status: Pending

Showing detailed mappings for 20 of 28 requirements.

Appendix E: References

End of Report - Generated by Security Requirements Analysis System v2.0 Generated: 2025-11-19 19:04:00

--- title: "Security Requirements Analysis Report" subtitle: "Comprehensive Security Analysis with Interactive Dashboard" author: "Security Requirements System v2.0" date: "2025-11-19 19:04:00" format: html: page-layout: full toc: true toc-depth: 3 toc-location: left embed-resources: true code-fold: true code-tools: true fig-width: 8 fig-height: 5 fig-dpi: 300 number-sections: false smooth-scroll: true mermaid-format: svg execute: echo: false warning: false message: false freeze: auto jupyter: python3 --- *Generated: 2025-11-19 19:04:00* *Report Version: 2.0 - Comprehensive Security Analysis* --- ## 1. Executive Summary This section provides a high-level overview of the security requirements analysis, presenting key findings, validation results, and an interactive dashboard for stakeholders and decision-makers. The executive summary enables rapid comprehension of the security posture, critical risks, control coverage, and compliance status without requiring detailed technical knowledge. ### 1.1. Purpose and Scope **Purpose** This document presents a comprehensive security requirements analysis for the proposed application, systematically mapping high-level business requirements to specific, actionable security controls aligned with multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. The analysis provides a complete security requirements specification that guides secure system design, implementation, and verification. **Scope** This analysis encompasses all functional requirements provided, delivering comprehensive coverage across multiple security domains: - **Requirements Analysis**: Systematic decomposition and security-relevant extraction from business requirements - **Stakeholder Analysis**: Identification of stakeholders, trust boundaries, and security responsibilities - **Threat Modeling**: Systematic identification and assessment of security threats using STRIDE methodology - **Security Control Mapping**: Mapping requirements to multi-standard security controls (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed implementation guidance - **Compliance Requirements**: Identification of regulatory and legal compliance obligations - **Architectural Security**: Security architecture recommendations and design patterns - **Implementation Planning**: Prioritized, phased implementation roadmap - **Verification Strategies**: Testing and validation approaches for security controls The analysis provides both strategic guidance for security planning and tactical details for implementation teams. ### 1.2. Key Findings This section summarizes the most critical results from the security requirements analysis, providing executives and stakeholders with immediate insight into the security posture and validation status. **Analysis Metrics** - **Validation Score**: 0.92/1.0 - **Validation Status**: ✅ Passed - **Analysis Iterations**: 1 - **Requirements Analyzed**: 25 **Application Summary** An AI-powered code review and security analysis platform that integrates with Git repositories to automatically analyze commits and codebases (via LLM and static analysis), identify security vulnerabilities and secrets, score and explain risks, propose and optionally create fixes (PRs), support team collaboration and CI/CD integration, manage and serve models (inference and training) securely, and provide compliance reporting and audit logging while protecting sensitive source code, tokens, and configuration data. The validation score reflects the quality and completeness of the security requirements across five dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. A score of 0.8 or higher indicates that the requirements are ready for implementation, while scores below this threshold may require refinement before proceeding. ### 1.3. Security Overview Dashboard This interactive dashboard provides executive-level visualization of key security metrics and trends, enabling rapid assessment of the security posture through intuitive charts and data visualizations. The dashboard presents critical information across multiple dimensions: risk distribution, security control coverage, compliance status, implementation progress, and data quality metrics. For optimal viewing experience, render this document with Quarto to enable interactive chart functionality, allowing stakeholders to explore data dynamically and drill down into specific areas of interest. ::: {.panel-tabset} #### Risk ```{python} #| label: load-metrics #| echo: false #| warning: false import json import pandas as pd from pathlib import Path # Load dashboard data artifacts artifacts_path = Path("artifacts_20251119_190400") threats = pd.read_json(artifacts_path / "threats.json") asvs = pd.read_json(artifacts_path / "asvs_mapping.json") priorities = pd.read_json(artifacts_path / "priorities.json") compliance = pd.read_json(artifacts_path / "compliance.json") delivery = pd.read_json(artifacts_path / "delivery.json") coverage = pd.read_json(artifacts_path / "coverage.json") validation = json.loads((artifacts_path / "validation.json").read_text()) ``` ```{python} #| label: fig-risk-heatmap #| fig-cap: "Risk heat map showing threat distribution by likelihood and impact (1-5 scale)." #| echo: false #| warning: false try: import plotly.express as px import plotly.graph_objects as go # Create risk heatmap heat = threats.groupby(["likelihood", "impact"]).size().reset_index(name="count") # Create a complete 5x5 grid import numpy as np l_range = range(1, 6) i_range = range(1, 6) grid_data = [] for l in l_range: for i in i_range: count = heat[(heat["likelihood"] == l) & (heat["impact"] == i)]["count"].sum() grid_data.append({"likelihood": l, "impact": i, "count": int(count)}) heat_df = pd.DataFrame(grid_data) pivot = heat_df.pivot(index="impact", columns="likelihood", values="count").fillna(0) fig_risk = go.Figure(data=go.Heatmap( z=pivot.values, x=[f"L{x}" for x in pivot.columns], y=[f"I{y}" for y in pivot.index], text=pivot.values, texttemplate="%{text:.0f}", textfont={"size": 12}, colorscale="YlOrRd", hoverongaps=False )) fig_risk.update_layout( title="Threat Risk Matrix (Likelihood × Impact)", xaxis_title="Likelihood →", yaxis_title="Impact →", height=400 ) fig_risk.show() except Exception as e: print(f"⚠️ Could not generate risk heatmap: {e}") print("\nRisk Distribution (Static):") print(threats.groupby("risk_level").size()) ``` **Top 5 Highest Risks:** ```{python} #| echo: false #| output: asis #| warning: false top_risks = threats.nlargest(5, ["likelihood", "impact"]) for idx, risk_row in top_risks.iterrows(): risk_id = risk_row['id'] risk_level = risk_row['risk_level'] component = risk_row.get('component', 'Unknown Component') category = risk_row.get('category', 'N/A') likelihood = risk_row.get('likelihood', 'N/A') impact = risk_row.get('impact', 'N/A') description = risk_row['description'] print(f"**{risk_id}** ({risk_level}) - {component}") print(f"- **Category:** {category}") print(f"- **Likelihood:** {likelihood} | **Impact:** {impact}") print(f"- **Description:** {description}") print("") ``` #### Controls ```{python} #| label: fig-standard-distribution #| fig-cap: "Security control distribution by standard (OWASP, NIST, ISO 27001)." #| echo: false #| warning: false try: import plotly.express as px # Group by standard if "standard" in asvs.columns: dist = asvs.groupby("standard").size().reset_index(name="controls") dist = dist.sort_values("controls", ascending=False) # Add percentage for better context total = dist["controls"].sum() dist["percentage"] = (dist["controls"] / total * 100).round(1) # Map standard codes to display names standard_names = { "OWASP": "OWASP ASVS", "NIST": "NIST SP 800-53", "ISO27001": "ISO 27001:2022" } dist["standard_name"] = dist["standard"].map(standard_names).fillna(dist["standard"]) fig_std = px.bar( dist, x="standard_name", y="controls", text=[f"{c}<br>({p}%)" for c, p in zip(dist["controls"], dist["percentage"])], title="Security Controls by Standard", labels={"standard_name": "Security Standard", "controls": "Control Count"}, color="controls", color_continuous_scale="Blues" ) fig_std.update_traces(textposition='outside') fig_std.update_layout(height=400, showlegend=False) fig_std.show() else: print("Standard distribution data not available.") except Exception as e: print(f"⚠️ Could not generate standard distribution chart: {e}") ``` ```{python} #| label: fig-asvs-distribution #| fig-cap: "OWASP ASVS control distribution by verification category (V1-V14)." #| echo: false #| warning: false try: import plotly.express as px # Filter to OWASP controls only asvs_owasp = asvs[asvs["standard"] == "OWASP"] if "standard" in asvs.columns else asvs # Group by ASVS category dist = asvs_owasp.groupby("v_category").size().reset_index(name="controls") dist = dist.sort_values("v_category") # Add percentage for better context total = dist["controls"].sum() dist["percentage"] = (dist["controls"] / total * 100).round(1) if total > 0 else 0 fig_asvs = px.bar( dist, x="v_category", y="controls", text=[f"{c}<br>({p}%)" for c, p in zip(dist["controls"], dist["percentage"])], title="OWASP ASVS Controls by Verification Category", labels={"v_category": "ASVS Category", "controls": "Control Count"}, color="controls", color_continuous_scale="Blues" ) fig_asvs.update_traces(textposition='outside') fig_asvs.update_layout(height=400, showlegend=False) fig_asvs.show() except Exception as e: print(f"⚠️ Could not generate ASVS chart: {e}") ``` ```{python} #| label: fig-priority-breakdown #| fig-cap: "Security control priority distribution (Critical/High/Medium/Low)." #| echo: false #| warning: false try: import plotly.express as px # Priority breakdown with color mapping color_map = { "Critical": "#c62828", "High": "#f57c00", "Medium": "#fbc02d", "Low": "#388e3c" } # Sort by priority level priority_order = ["Critical", "High", "Medium", "Low"] priorities_sorted = priorities.set_index("level").reindex(priority_order).reset_index() fig_prio = px.bar( priorities_sorted, x="level", y="count", text="count", title="Control Priority Breakdown", labels={"level": "Priority Level", "count": "Number of Controls"}, color="level", color_discrete_map=color_map ) fig_prio.update_traces(textposition='outside') fig_prio.update_layout(height=400, showlegend=False) fig_prio.show() except Exception as e: print(f"⚠️ Could not generate priority chart: {e}") ``` **Coverage Metrics:** ```{python} #| echo: false #| output: asis #| warning: false total_controls = len(asvs) total_reqs = len(coverage) req_coverage = (coverage["has_controls"].mean() * 100) if not coverage.empty else 0 verif_coverage = (coverage["tests"].gt(0).mean() * 100) if not coverage.empty else 0 # Calculate additional metrics avg_controls_per_req = (total_controls / total_reqs) if total_reqs > 0 else 0 critical_controls = len(asvs[asvs["priority"] == "Critical"]) if "priority" in asvs.columns else 0 # Calculate standard distribution if available if "standard" in asvs.columns: std_counts = asvs["standard"].value_counts() owasp_count = std_counts.get("OWASP", 0) nist_count = std_counts.get("NIST", 0) iso_count = std_counts.get("ISO27001", 0) print(f"- **Total Security Controls Mapped:** {total_controls}") print(f" - OWASP ASVS: {owasp_count} controls") print(f" - NIST SP 800-53: {nist_count} controls") print(f" - ISO 27001: {iso_count} controls") else: print(f"- **Total Security Controls Mapped:** {total_controls} (OWASP ASVS, NIST SP 800-53, ISO 27001)") print(f"- **Requirements with Security Control Mapping:** {req_coverage:.1f}% ({coverage['has_controls'].sum()}/{total_reqs})") print(f"- **Average Controls per Requirement:** {avg_controls_per_req:.1f}") print(f"- **Critical Controls:** {critical_controls} ({critical_controls/total_controls*100:.1f}% of total)" if total_controls > 0 else "- **Critical Controls:** {critical_controls}") print(f"- **Requirements with Verification:** {verif_coverage:.1f}% ({coverage['tests'].gt(0).sum()}/{total_reqs})") print(f"- **Recommended ASVS Level:** L2 (Standard)") ``` #### Compliance ```{python} #| label: fig-compliance-rag #| fig-cap: "Compliance status across all applicable frameworks (Red-Amber-Green rating). Shows regulatory compliance (GDPR, HIPAA, PCI-DSS, etc.) and security standards (OWASP ASVS, NIST SP 800-53, ISO 27001)." #| echo: false #| warning: false try: import plotly.express as px # Show all compliance frameworks (regulatory and security standards) comp_filtered = compliance # Compliance RAG status status_map = { "Compliant": "#2e7d32", "In Progress": "#f9a825", "Gap": "#c62828" } # Sort frameworks alphabetically comp_sorted = comp_filtered.sort_values("framework") fig_comp = px.bar( comp_sorted, x="framework", y=[1] * len(comp_sorted), # Equal height bars color="status", color_discrete_map=status_map, title="Compliance Status Across All Frameworks", labels={"framework": "Framework", "y": ""}, text="status", height=400 ) fig_comp.update_layout(showlegend=True, yaxis_visible=False, yaxis_showticklabels=False) fig_comp.update_traces(textposition='inside') fig_comp.show() except Exception as e: print(f"⚠️ Could not generate compliance chart: {e}") print("\nCompliance Status (Static):") for _, row in compliance.iterrows(): print(f"- **{row['framework']}**: {row['status']}") ``` **Compliance Summary:** ```{python} #| echo: false #| output: asis #| warning: false for _, row in compliance.iterrows(): status_icon = "✅" if row["status"] == "Compliant" else "⚠️" if row["status"] == "In Progress" else "❌" print(f"- {status_icon} **{row['framework']}**: {row['status']} (Next Audit: {row['next_audit']})") ``` #### Delivery ```{python} #| label: fig-delivery-burndown #| fig-cap: "Projected implementation timeline by phase and week (based on priority-based planning)." #| echo: false #| warning: false try: import plotly.express as px import plotly.graph_objects as go # Create burndown chart with planned vs projected completion fig_burn = go.Figure() for phase in delivery["phase"].unique(): phase_data = delivery[delivery["phase"] == phase] # Planned line fig_burn.add_trace(go.Scatter( x=phase_data["week"], y=phase_data["planned"], mode='lines', name=f"{phase} (Planned)", line=dict(dash='dash', color='gray'), showlegend=True )) # Projected completion line fig_burn.add_trace(go.Scatter( x=phase_data["week"], y=phase_data["completed"], mode='lines+markers', name=f"{phase} (Projected)", showlegend=True )) fig_burn.update_layout( title="Security Controls Implementation Timeline (Projected)", xaxis_title="Week", yaxis_title="Number of Controls", height=400, hovermode='x unified' ) fig_burn.show() except Exception as e: print(f"⚠️ Could not generate delivery chart: {e}") ``` **Implementation Timeline (Projected):** ```{python} #| echo: false #| output: asis #| warning: false phase1 = delivery[delivery["phase"].str.contains("Phase 1")] phase2 = delivery[delivery["phase"].str.contains("Phase 2")] if not phase1.empty: p1_progress = (phase1["completed"].iloc[-1] / phase1["planned"].iloc[-1] * 100) if phase1["planned"].iloc[-1] > 0 else 0 print(f"- **Phase 1 (Critical/High):** {p1_progress:.0f}% projected completion (Weeks 1-8)") if not phase2.empty: p2_progress = (phase2["completed"].iloc[-1] / phase2["planned"].iloc[-1] * 100) if phase2["planned"].iloc[-1] > 0 else 0 print(f"- **Phase 2 (Medium):** {p2_progress:.0f}% projected completion (Weeks 9-16)") print(f"- **Phase 3 (Low/Ongoing):** Continuous improvement and monitoring") print(f"") print(f"*Note: Timeline is based on priority-based planning and assumes steady implementation progress.*") ``` #### Data Quality **Validation Metrics:** ```{python} #| echo: false #| output: asis #| warning: false val_score = validation.get("score", 0) val_passed = validation.get("passed", False) dims = validation.get("dims", {}) status_icon = "✅" if val_passed else "⚠️" if val_score >= 0.7 else "❌" print(f"") print(f"**Overall Validation Score:** {status_icon} {val_score:.2f}/1.0") print(f"") if dims: print("**Dimension Scores:**") print(f"") for dim, score in dims.items(): dim_icon = "✅" if score >= 0.8 else "⚠️" if score >= 0.7 else "❌" print(f"- {dim_icon} **{dim.capitalize()}:** {score:.2f}") else: print("*Dimension scores not available.*") ``` ```{python} #| label: fig-data-quality #| fig-cap: "Data quality and coverage metrics." #| echo: false #| warning: false try: import plotly.graph_objects as go # Calculate quality metrics threats_linked_pct = (coverage["has_threat"].mean() * 100) if not coverage.empty else 0 # Calculate average controls per requirement avg_controls_per_req = (total_controls / total_reqs) if total_reqs > 0 else 0 # For the chart, we'll show percentage-based metrics and handle avg controls separately metrics = { "Requirements Mapped": req_coverage, "Threats Linked": threats_linked_pct, "Verification Coverage": verif_coverage, "Avg Controls/Req": avg_controls_per_req * 20, # Scale for visual comparison (multiply by 20 to fit chart scale) "Validation Score": val_score * 100 } # Create custom text labels (show actual values for avg controls, percentages for others) text_labels = [] for key, val in metrics.items(): if key == "Avg Controls/Req": text_labels.append(f"{avg_controls_per_req:.1f}") else: text_labels.append(f"{val:.1f}%") fig_quality = go.Figure(go.Bar( x=list(metrics.values()), y=list(metrics.keys()), orientation='h', text=text_labels, textposition='outside', marker=dict( color=list(metrics.values()), colorscale='RdYlGn', cmin=0, cmax=100 ) )) fig_quality.update_layout( title="Data Quality & Coverage Metrics", xaxis_title="Score / Percentage (%)", xaxis=dict(range=[0, 110]), height=400, showlegend=False ) fig_quality.show() except Exception as e: print(f"⚠️ Could not generate quality chart: {e}") ``` ```{python} #| echo: false #| output: asis #| warning: false # Parser and data quality stats total_entries = len(coverage) with_threats = coverage["has_threat"].sum() with_controls = coverage["has_controls"].sum() with_tests = coverage["tests"].gt(0).sum() print(f"") print(f"**Traceability Matrix:**") print(f"") print(f"- Total Requirements: {total_entries}") print(f"- Linked to Threats: {with_threats} ({with_threats/max(total_entries,1)*100:.1f}%)") print(f"- Mapped to Security Controls: {with_controls} ({with_controls/max(total_entries,1)*100:.1f}%)") print(f"- With Verification: {with_tests} ({with_tests/max(total_entries,1)*100:.1f}%)") print(f"") print(f"**Data Quality:** {"✅ Excellent" if val_score >= 0.8 else "⚠️ Good" if val_score >= 0.7 else "❌ Needs Improvement"}") ``` ::: --- ## 2. Requirements Understanding This section presents a comprehensive analysis of the functional requirements, extracting security-relevant information and establishing the foundation for the security requirements specification. Understanding the functional requirements is essential for identifying security implications, data sensitivity, trust boundaries, and security-critical components. This analysis transforms business requirements into security-aware specifications that inform threat modeling, control selection, and compliance assessment. ### 2.1. High-Level Requirements Analysis The following high-level functional requirements have been identified and analyzed for security implications: 1. Repository integration with GitHub, GitLab, and Bitbucket (OAuth + webhooks) and support for real-time commit monitoring 2. Multi-language source code analysis (Python, Java, JavaScript, Go, etc.) 3. LLM-based code review engine (inference API) with fine-tuned models and model versioning 4. Static code analysis and pattern recognition for security anti-patterns 5. Dependency scanning for known CVEs and supply-chain vulnerabilities 6. Secret detection in code and configuration files 7. AI-generated remediation suggestions including code snippets and contextual explanations 8. Automatic pull request creation and optional auto-apply fixes with approval workflows 9. Severity scoring, risk assessment, and contextual prioritization 10. Developer dashboards with personalized security metrics and trend analysis 11. Issue comment threads, collaboration features, and integration with Slack/Teams notifications 12. Integration with CI/CD pipelines for pre-merge and post-merge scanning 13. OWASP Top 10 and custom security policy enforcement with customizable rules 14. Audit logging and immutable records of findings, decisions, and remediation history 15. Exportable compliance reports and evidence packages 16. Model management: inference API endpoints, versioning, A/B testing, performance monitoring, and drift detection 17. Custom model training on organization codebases with privacy-preserving controls 18. Secure storage and handling of code snippets, tokens, keys, and analysis results (encryption, retention, redaction) 19. Vulnerability database management and mapping to CVE/NVD feeds 20. Caching of analysis results for performance with cache invalidation policies 21. Access control and RBAC for repo access, model access, and UI/API operations 22. Rate limiting, abuse prevention, and API authentication/authorization 23. Support for on-premises or isolated VPC/SaaS deployment models and data residency controls 24. Consent, data usage policies, and ability to opt-in/opt-out model training on customer code 25. Monitoring, alerting, and incident response for platform security (SIEM integration) ### 2.2. Detailed Requirements Breakdown | Req ID | Requirement | Business Category | Security Sensitivity | Data Classification | |--------|-------------|-------------------|---------------------|---------------------| | REQ-001 | Repository integration with GitHub, GitLab, and Bi... | Repository Integration / Authentication | High | Confidential | | REQ-002 | Support multi-language parsing and analysis for Py... | Analysis Engine | Medium | Internal | | REQ-003 | Provide an LLM-based code review engine exposed vi... | Model Management / Inference | High | Confidential | | REQ-004 | Perform static code analysis (SAST) and pattern re... | Analysis Engine / Security Testing | High | Confidential | | REQ-005 | Dependency scanning that identifies libraries, ver... | Dependency Management / Vulnerability Management | High | Internal | | REQ-006 | Secret detection across code, config files, and co... | Secret Management / Data Protection | High | Restricted | | REQ-007 | Generate AI remediation suggestions with code snip... | Remediation / Developer Productivity | Medium | Confidential | | REQ-008 | Support automatic pull request creation with propo... | Automation / CI/CD Integration | High | Confidential | | REQ-009 | Provide severity scoring and risk assessment that ... | Risk Management | Medium | Internal | | REQ-010 | Context-aware explanations for each finding, inclu... | Reporting / Developer Support | Medium | Internal | | REQ-011 | Developer dashboards displaying personalized secur... | Team Collaboration / Analytics | Medium | Internal | | REQ-012 | Support comment threads on identified issues, assi... | Collaboration / Workflow | Medium | Internal | | REQ-013 | Integrate with CI/CD pipelines (e.g., GitHub Actio... | CI/CD Integration | High | Confidential | | REQ-014 | Send configurable notifications for critical findi... | Notifications / Collaboration | Medium | Internal | | REQ-015 | Provide OWASP Top 10 checks and allow creation/enf... | Compliance / Policy Enforcement | Medium | Internal | | REQ-016 | Maintain immutable audit logs of all analyses, fin... | Audit / Compliance | High | Restricted | | REQ-017 | Exportable compliance reports and evidence package... | Reporting / Compliance | Medium | Internal | | REQ-018 | Model management features including model versioni... | Model Management / Governance | High | Internal | | REQ-019 | Enable custom model training on customers' codebas... | Model Training / Data Privacy | High | Restricted | | REQ-020 | Model performance monitoring and drift detection w... | Model Monitoring / Quality | Medium | Internal | | REQ-021 | Secure processing and storage of code snippets, an... | Data Handling / Security | High | Restricted | | REQ-022 | Maintain and update a vulnerability database mappe... | Vulnerability Management / Threat Intelligence | Medium | Internal | | REQ-023 | Cache analysis results for performance with secure... | Performance / Data Management | Medium | Confidential | | REQ-024 | Implement fine-grained access control and RBAC for... | Authentication / Authorization | High | Restricted | | REQ-025 | API gateway protections: API key management, OAuth... | API Security / Operational Security | High | Internal | | REQ-026 | Support deployment options: SaaS with VPC peering,... | Deployment / Infrastructure | High | Confidential | | REQ-027 | Data usage and consent controls including the abil... | Privacy / Data Governance | High | Restricted | | REQ-028 | Integrate platform monitoring with SIEM/SOAR for s... | Monitoring / Incident Response | High | Internal | ### 2.3. Security Context and Regulatory Obligations Applicable regulations and frameworks include: GDPR (personal data processing and cross-border data transfers) and national data protection laws for EU customers; CCPA/CPRA for California residents; SOC 2 Type II controls and ISO 27001 for information security management; NIST SP 800-53 / NIST CSF for federal or enterprise customers; OWASP Application Security standards (including OWASP Top 10) for web and API security; CWE/CVE/NVD for vulnerability classification; export control restrictions for cryptographic technologies; and industry-specific regulations as applicable (e.g., HIPAA if platform processes healthcare-related code containing PHI, PCI-DSS if code or artifacts contain payment card data). Legal obligations around intellectual property, contractual non-disclosure agreements, and customer-specific data residency or sovereignty requirements must be observed. Where models are trained on customer data, intellectual property and confidentiality clauses govern permissible use. Privacy expectations necessitate data minimization, consent, and the ability to erase or export customer data on request. ### 2.4. Assumptions - Platform is primarily cloud-hosted but must offer private SaaS/VPC or on-premises deployment options for customers with stricter controls. - Customers will provide OAuth consent and necessary repository scopes for analysis and webhook registration. - Users have internet access and modern browsers for the UI; CI/CD systems can reach the platform API endpoints. - Customers will provide clear authority to analyze and (optionally) train models on their codebases; legal contracts will define usage rights. - Encryption key management will be possible either via platform-managed KMS or customer-managed keys (BYOK) for enterprise customers. - Customers will classify their own data sensitivity; platform will annotate but not automatically override customer classification. - Real-time analysis is expected for commits via webhooks, while deeper analysis of full repositories can be scheduled asynchronously. - Third-party services (e.g., Slack, Teams, model hosting providers) used by the platform will meet security integration requirements (e.g., support for secure webhooks, tokens). ### 2.5. Constraints - Must support multiple Git providers (GitHub/GitLab/Bitbucket) and their differing OAuth/webhook APIs and permission models. - Must respect data residency and sovereignty constraints; some customers will require on-prem or single-tenant deployments. - Performance constraints: inference latency and scalability for LLMs are bounded by available compute and cost; caching and async workflows needed to meet SLAs. - Legal/contractual constraints: cannot use customer code for global model training without explicit opt-in and must provide deletion/export mechanisms. - Operational constraints: must maintain timely CVE feed updates and vulnerability intelligence; third-party feed outages must be handled gracefully. - Security constraints: all sensitive data must be encrypted in transit and at rest; support BYOK for enterprise customers and hardened secrets management. - Integration constraints: must integrate with diverse CI/CD tools and enterprise SSO systems (SAML/OIDC) with varying configuration complexity. - Model governance constraints: A/B testing and model rollouts must avoid exposing customer code to unapproved model variants and must track provenance and drift. - Availability constraints: platform must provide SLAs for availability and define maintenance windows, with options for higher availability in enterprise plans. - Compliance constraints: audit log retention periods, reporting formats, and evidence packages must satisfy multiple regulatory regimes and be configurable. --- ## 3. Stakeholder Analysis This section identifies and analyzes all stakeholders involved in or affected by the system, including users, administrators, external partners, and regulatory bodies. Stakeholder analysis establishes trust boundaries, defines security responsibilities, and identifies potential security concerns from different stakeholder perspectives. Understanding stakeholder relationships and trust boundaries is critical for designing appropriate access controls, authentication mechanisms, and data protection measures. ### 3.1. Identified Stakeholders and User Personas | Role | Privilege Level | Trust Level | Key Security Concerns | |---------------------------------|-----------------|-------------------|-----------------------------------------------------------------------| | Developer | User | Trusted | Unauthorized access to code repositories, exposure of sensitive data, insecure code practices. | | Security Analyst | User | Trusted | Misidentification of vulnerabilities, reliance on faulty AI suggestions, data leakage during analysis. | | System Administrator | Admin | Trusted | Privilege escalation by insiders, account compromise leading to system-wide access, misconfiguration of access controls. | | Repository Owner | User | Trusted | Unauthorized access to repositories, exposure of sensitive tokens and keys, code integrity issues. | | CI/CD Pipeline Service Account | Service Account | Partially Trusted | Insecure API integration, unintended code execution leading to vulnerabilities, exposure of build secrets. | | External Integrator (Git Providers) | Service Account | Partially Trusted | Insecure OAuth integration, unauthorized access to code commits, potential data leakage. | | AI Model Service Account | Service Account | Partially Trusted | Model exposure through insecure APIs, unauthorized access to training data, manipulation of inference results. | | Compliance Auditor | User | Partially Trusted | Incomplete audit trails, unauthorized access to security findings, inability to verify compliance. | | Notification System (Slack/Teams) | Service Account | Untrusted | Potential for unauthorized notifications revealing sensitive information, lack of encryption during data transfer. | | Vulnerability Database Service | Service Account | Untrusted | Exposure of CVE data leading to exploitation, unauthorized access to vulnerability findings. | ### 3.2. Trust Model Trust boundaries are established at the user interface, API gateway, backend services, and data storage levels. Security mechanisms enforcing these boundaries include OAuth-based authentication for secure repository access, role-based access control (RBAC) to ensure users can only access data and functionalities pertinent to their roles, and network segmentation to limit exposure to critical services. Developers have access to their own repositories and analysis results, while Security Analysts can access vulnerability findings for remediation. System Administrators have comprehensive management access, while service accounts like CI/CD integrations and AI Model accounts operate under strict RBAC policies to minimize exposure. The principle of least privilege is implemented by ensuring that users and service accounts are granted only the permissions necessary for their specific tasks, effectively reducing the risk of unauthorized access and privilege escalation across the system. --- ## 4. System Architecture Analysis ### 4.1. Architectural Overview The platform is a cloud-first, optionally private/SaaS system composed of a secure edge/API gateway and frontend for developers and CI/CD, core application services that orchestrate analysis workflows, an AI-powered analysis engine (LLM inference + SAST + dependency/secret scanning), a model management plane for training/versioning/inference, and a data layer that securely stores code artifacts, vulnerability data, audit logs, caches, and models. External integrations include Git providers (OAuth/webhooks), CVE/NVD feeds, notification systems (Slack/Teams), CI/CD pipelines, SSO providers, managed model hosts/GPUs, and SIEM. Components communicate via authenticated HTTPS APIs and message queues for async processing; sensitive artifacts and keys are encrypted and access-controlled via RBAC and KMS/BYOK. ### 4.2. Architecture Diagram <style> svg { width: 100% !important; max-width: 100% !important; } .nodeLabel { white-space: normal !important; } </style> ```{mermaid} %%{init: {'theme':'dark'}}%% graph TB subgraph "Edge Layer" Users[Developers CI-CD Agents] --> API[API Gateway & Auth] end subgraph "Frontend Layer" API --> FE[Web App SPA & Dashboards] end subgraph "Application Services" API --> Orch[Orchestration & Core App Services] Orch --> Analysis[AI Analysis Engine LLM+SAST] Orch --> ModelMgmt[Model Management & Inference API] Orch --> Collab[Collaboration, Comments & Notifications] Orch --> CICD[CI/CD Integration & Webhook Handler] end subgraph "Data Layer" Analysis --> Cache[Secure Cache Results] Analysis --> Artifacts[Encrypted Code Artifacts Storage] ModelMgmt --> ModelStore[Model Store & Versioning] Orch --> VDB[Vulnerability DB & CVE Metadata] Orch --> Audit[Immutable Audit Logs & Reports] KMS[KMS & Secrets Manager] end subgraph "External Services" Users --> Git[GitHub GitLab Bitbucket] Orch --> Slack[Slack & Microsoft Teams] VDB --> CVE[NVD CVE Feeds] ModelStore --> GPU[Managed GPU / Model Host] API --> SSO[SSO SAML/OIDC] Audit --> SIEM[SIEM / SOAR] end Artifacts --> Git KMS --> Artifacts KMS --> ModelStore Orch --> SIEM ``` ### 4.3. Component Breakdown | Component | Responsibility | Security Criticality | External Dependencies | |-----------|----------------|---------------------|----------------------| | Edge/API Gateway | Handle ingress traffic, OAuth flows, JWT... | Critical | SSO SAML/OIDC providers, WAF / DDoS protection | | Frontend Layer | Web SPA and dashboards for developers an... | Medium | Browser clients, API Gateway | | Application Services | Core orchestration, business logic, work... | Critical | GitHub/GitLab/Bitbucket APIs, CI/CD systems (GitHub Actions, Jenkins) | | AI Analysis Engine | Perform LLM-based code review, static an... | Critical | Managed model hosts / GPU providers, Local/on-prem model infra (for private deployments) | | Model Management | Host inference APIs, version models, man... | High | GPU/Model hosting providers, Model training pipelines | | Data Storage & Persistence | Securely store code artifacts, vulnerabi... | Critical | KMS / BYOK providers, Immutable storage providers (WORM/append-only) | | External Integrations | Connectors to third-party systems: Git p... | High | GitHub GitLab Bitbucket APIs, NVD/CVE feeds | ### 4.4. Data Flow Analysis Developers or CI/CD send commits/webhook events to the Edge/API Gateway which authenticates requests and forwards them to Orchestration. Orchestration fetches code via repository APIs (OAuth-scoped), stores encrypted artifacts, and enqueues analysis tasks. The AI Analysis Engine runs LLM inference and SAST, consults the Vulnerability DB, and writes findings to Audit Logs and Data Storage; remediation suggestions are generated and optionally open PRs back to the Git provider. Model Management serves inference endpoints and may receive opt-in training data in isolated environments. Results are cached for performance; dashboards and notifications surface findings. Sensitive data (code, tokens, secrets) are encrypted at rest and in transit; audit logs and exports are produced on demand. ### 4.5. Attack Surface Analysis Primary entry points: API Gateway endpoints (inference APIs, REST APIs), OAuth webhook endpoints from Git providers, and the Web UI. These are high-risk and require strong authn/authz, rate limiting, WAF, and webhook signature validation. Integration points with Git providers and CI/CD are exposed via OAuth tokens and webhooks; compromised tokens can expose code—mitigate with least-privilege scopes, token rotation, and monitoring. Model inference endpoints are sensitive (risk of data exfiltration and prompt injection) and should enforce input sanitization, access controls, rate limits, and logging; A/B testing and training endpoints add risk for model leakage and must be isolated and opt-in. Data storage endpoints (artifact stores, caches, vulnerability DB, audit logs) represent a critical defensive boundary—protect with encryption, KMS, and strict IAM. Notification integrations (Slack/Teams) risk accidental exposure of secrets; notifications must redact code and include role-based visibility. Administrative interfaces and model management UIs need MFA and SSO enforcement. Overall risk priorities: OAuth/webhook integrations (High), Inference APIs and model training surfaces (High), Data stores and audit logs (Critical), Web UI and API Gateway (High), External feeds and notifications (Medium). Continuous monitoring, SIEM integration, automated anomaly detection, and incident response play a central role in reducing residual risk. --- ## 5. Threat Modeling This section presents a comprehensive threat analysis of the system architecture and functional requirements. Threat modeling systematically identifies potential security vulnerabilities and attack vectors, enabling proactive risk mitigation through the application of appropriate security controls. ### 5.1. Threat Modeling Methodology This analysis employs the **STRIDE** threat modeling methodology, a systematic framework developed by Microsoft for identifying security threats across six categories: - **Spoofing Identity**: Threats involving impersonation of users or systems - **Tampering with Data**: Threats involving unauthorized modification of data or system components - **Repudiation**: Threats where users deny performing actions (lack of non-repudiation) - **Information Disclosure**: Threats involving unauthorized access to sensitive information - **Denial of Service**: Threats causing disruption or unavailability of system services - **Elevation of Privilege**: Threats allowing unauthorized access to privileged functions For each identified threat, the analysis evaluates **likelihood** (attack complexity and exposure) and **impact** (potential damage to confidentiality, integrity, or availability) to determine overall **risk level**. The methodology ensures comprehensive coverage of security concerns across all system components and interfaces. ### 5.2. Threat Analysis and Risk Assessment #### 5.2.1. Threat Overview The following table provides a quick reference of all identified threats. Detailed analysis including descriptions, mitigation strategies, and residual risk assessment (where available) is provided in the section below. | Threat ID | Component | Category | Risk Level | Likelihood | Impact | |-----------|-----------|----------|------------|-----------|--------| | THR-001 | Edge/API Gateway | Spoofing | Critical | High | High | | THR-009 | AI Analysis Engine | Information Disclosure | Critical | High | High | | THR-002 | Edge/API Gateway | Tampering | High | Medium | High | | THR-003 | Edge/API Gateway | Denial of Service | High | Medium | High | | THR-004 | Frontend Layer | Information Disclosure | High | Medium | High | | THR-006 | Application Services | Tampering | High | Medium | High | | THR-008 | Application Services | Information Disclosure | High | Medium | High | | THR-010 | AI Analysis Engine | Tampering | High | Medium | High | | THR-011 | AI Analysis Engine | Elevation of Privilege | High | Medium | High | | THR-012 | AI Analysis Engine | Denial of Service | High | High | Medium | | THR-014 | Secret Detection / Data Storage | Information Disclosure | High | Medium | High | | THR-015 | Model Management | Tampering | High | Medium | High | | THR-016 | Model Management | Information Disclosure | High | Medium | High | | THR-017 | Data Storage & Persistence | Information Disclosure | High | Medium | High | | THR-018 | Data Storage & Persistence | Tampering | High | Medium | High | | THR-019 | External Integrations | Spoofing | High | Medium | High | | THR-020 | External Integrations | Information Disclosure | High | Medium | High | | THR-022 | Repository Integration (OAuth) | Spoofing | High | Medium | High | | THR-023 | CI/CD Integrations | Elevation of Privilege | High | Medium | High | | THR-026 | Data Handling / Caching | Information Disclosure | High | Medium | High | | THR-027 | Application Services / APIs | Tampering | High | Medium | High | | THR-028 | Edge/API Gateway | Elevation of Privilege | High | Low | High | | THR-005 | Frontend Layer | Spoofing | Medium | Medium | Medium | | THR-007 | Application Services | Repudiation | Medium | Medium | Medium | | THR-013 | AI Analysis Engine / Vulnerability Feeds | Information Disclosure | Medium | Medium | Medium | | THR-021 | External Integrations | Denial of Service | Medium | Medium | Medium | | THR-024 | Compliance and Reporting | Repudiation | Medium | Low | Medium | | THR-025 | Team Collaboration | Information Disclosure | Medium | Medium | Medium | **Total Threats Identified:** 28 #### 5.2.2. Detailed Threat Analysis This section provides comprehensive analysis of each identified threat, including descriptions, mitigation strategies, and residual risk assessment (where controls have been evaluated). Threats are organized by risk level for prioritized review. ##### Critical Risk Threats **THR-001** - Edge/API Gateway - **Category:** Spoofing - **Likelihood:** High | **Impact:** High - **Initial Risk Level:** Critical - **Description:** Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsigned/weakly-signed tokens) allowing an attacker to impersonate users or service accounts and access repositories, create scans, or view results. - **Mitigation Strategy:** Enforce strict OAuth flows and short-lived tokens, validate JWT signatures and claims (iss/aud/exp), use proof-of-possession or mTLS for high-priv tokens, rotate tokens frequently, require token binding for webhooks, monitor for suspicious token usage, rate-limit token usage, and implement device/session fingerprinting. Enforce scope minimization on OAuth clients. - **Controls Applied:** OAuth best practices, JWT signature validation, mTLS, Token rotation, WAF - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-009** - AI Analysis Engine - **Category:** Information Disclosure - **Likelihood:** High | **Impact:** High - **Initial Risk Level:** Critical - **Description:** Sensitive source code or secrets are transmitted to or stored on external managed model hosts (SaaS model providers) where tenant separation, contractual protections, or inadvertent logging could lead to leakage of proprietary code or credentials. - **Mitigation Strategy:** Offer private/on-prem model deployment options, require E2E encryption (client to model), ensure model hosts provide data isolation contracts, avoid logging raw code snippets at the model host, minimize data sent to external providers (use extracted features), use BYOK and VPC peering for private deployments, and provide explicit opt-in for sending full code to managed hosts. - **Controls Applied:** Private model deployments, E2E encryption, BYOK, Contractual data protections - **Control Effectiveness:** Medium - **Residual Risk Level:** High - **Status:** ⚠️ Requires Review ##### High Risk Threats **THR-002** - Edge/API Gateway - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Manipulation of inbound API requests or webhook payloads en route to backend (e.g., altering scan parameters, redirecting callbacks, or injecting malicious fields) to change scan behavior or escalate access. - **Mitigation Strategy:** Validate webhook signatures (HMAC), verify sender IPs where feasible, use mutual authentication (mTLS) for integrations, perform strict input validation and schema checks at gateway, sign and/or timestamp important parameters, and record an immutable request audit trail. - **Controls Applied:** Webhook signing (HMAC), Input validation, mTLS, WAF - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-003** - Edge/API Gateway - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** API gateway or edge infrastructure overwhelmed by high request volume or crafted requests (flooding, slowloris, large payloads) causing availability loss for scanning and developer access. - **Mitigation Strategy:** Implement rate limiting, request throttling, bot detection, WAF and DDoS protection, circuit breakers, autoscaling with quotas, request size limits, and progressive backoff for heavy analysis requests. Use API keys and quotas per tenant. - **Controls Applied:** WAF, Rate limiting, DDoS protection, Quotas - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-004** - Frontend Layer - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, findings or tokens to other users or remote hosts; stored XSS in comment threads revealing sensitive diffs. - **Mitigation Strategy:** Apply strict output encoding/escaping, implement a strong Content Security Policy (CSP), sanitize user inputs, adopt secure templating frameworks, use HttpOnly and Secure cookies, enforce same-site cookie attributes, and perform regular frontend security testing. - **Controls Applied:** CSP, Output encoding, Input sanitization - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-006** - Application Services - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Forged repository webhook events or API calls cause the application to perform unauthorized actions such as merging PRs, applying fixes, or changing RBAC settings. - **Mitigation Strategy:** Verify webhook signatures, use per-integration secrets, enforce least privilege for integration accounts, require branch protection and signed commits for automatic merges, and add manual approvals for high-impact automated fixes. - **Controls Applied:** Webhook signatures, Least privilege, Branch protection, Signed commits - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-008** - Application Services - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Broken access control leading to unauthorized users (or tenants) reading repository metadata, scan results, vulnerability details, or remediation suggestions. - **Mitigation Strategy:** Enforce RBAC/ABAC with least privilege, implement strong multi-tenant isolation, perform authorization checks server-side for each resource, adopt just-in-time access, and provide fine-grained data redaction/scoping. - **Controls Applied:** RBAC/ABAC, Multi-tenant isolation, Server-side authorization - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-010** - AI Analysis Engine - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Poisoning of training data or prompt injection via crafted code causing models to generate insecure or malicious remediation suggestions (e.g., suggesting credential exfiltration or insecure configurations). - **Mitigation Strategy:** Validate and sanitize training data, maintain provenance of training artifacts, isolate customer training sets, perform adversarial testing and red-team model evaluation, implement prompt and output filtering, and require deterministic SAST cross-checks for suggested fixes. - **Controls Applied:** Training data provenance, Adversarial testing, Output filters, SAST cross-validation - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-011** - AI Analysis Engine - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** LLM-generated remediation automatically introduces code that elevates privileges (e.g., creating backdoor admin endpoints or insecurely setting permissions) and is merged without adequate review. - **Mitigation Strategy:** Require human review or automated safety checks for high-risk code changes, implement automated SAST/IAST validation of suggested patches, prevent automatic merges for critical severity changes, and enforce branch protections and signed approvals for elevated-permission changes. - **Controls Applied:** Human-in-the-loop, SAST validation, Branch protection - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-012** - AI Analysis Engine - **Category:** Denial of Service - **Likelihood:** High | **Impact:** Medium - **Initial Risk Level:** High - **Description:** Excessive inference requests (automated or malicious) saturate model hosting or GPU capacity, causing analysis delays or denial of analysis service to other tenants. - **Mitigation Strategy:** Implement per-tenant quotas, rate-limiting, queueing/backpressure, autoscaling of inference clusters, graceful degradation to lightweight scanners, and cost-control safeguards. Monitor usage and detect anomalous patterns. - **Controls Applied:** Quotas, Rate limiting, Autoscaling, Usage monitoring - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-014** - Secret Detection / Data Storage - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Secrets (API keys, tokens, private keys) stored in persistent storage, caches, backups, logs, or remediation history in plaintext or weakly encrypted form leading to leak and unauthorized access to third-party systems. - **Mitigation Strategy:** Encrypt secrets at rest using KMS/BYOK, use envelope encryption, redact secrets from UI/logs, avoid storing full secrets (store hashed/hashed pointers), rotate secrets on detection, implement strict access controls and auditing for key material. - **Controls Applied:** KMS/BYOK, Envelope encryption, Secret redaction, Key rotation - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-015** - Model Management - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Unauthorized modification or replacement of model binaries or metadata (model poisoning or substitution) causing compromised inference results across tenants. - **Mitigation Strategy:** Secure model registries with RBAC, sign model artifacts, use integrity checks (hashes/signatures), store models in hardened immutable storage, require approvals for model changes, and log/model change audit trails. - **Controls Applied:** Model signing, RBAC for registry, Immutable storage, Change approvals - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-016** - Model Management - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Leakage of model binaries or customer-specific training data/artifacts used for fine-tuning (e.g., code used in training accessible to other customers or leaked externally). - **Mitigation Strategy:** Isolate customer training environments (VPC/on-prem), encrypt training datasets and artifacts, use access controls and logging for training storage, avoid multi-tenant mixing of data during training, and provide contractual controls for managed hosts. - **Controls Applied:** Training environment isolation, Encrypted storage, RBAC, BYOK - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-017** - Data Storage & Persistence - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Public exposure of backups, snapshots, or misconfigured storage buckets containing code artifacts, vulnerability DB, or audit logs (e.g., publicly accessible S3 bucket). - **Mitigation Strategy:** Enforce least-privilege IAM for storage, block public access by default, encrypt backups with KMS/BYOK, perform automated scans for open storage, enforce MFA and approval for snapshot exports, and implement immutable retention where required. - **Controls Applied:** Block public access, KMS encryption, IAM least privilege, Storage scanning - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-018** - Data Storage & Persistence - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Unauthorized modification or deletion of audit logs, remediation history, or vulnerability records to erase evidence or alter findings. - **Mitigation Strategy:** Store critical logs in WORM/append-only storage, forward copies to a hardened SIEM, apply strict IAM and separation of duties for log access, cryptographically sign logs, and enable integrity verification. - **Controls Applied:** WORM storage, SIEM forwarding, Signed logs, Separation of duties - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-019** - External Integrations - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed events or uses stolen integration credentials to manipulate the platform (trigger scans, inject malicious webhooks). - **Mitigation Strategy:** Enforce webhook signature verification, use short-lived integration credentials, require mutual TLS or IP allowlisting where possible, restrict delegated permissions, and monitor integration behavior for anomalies. - **Controls Applied:** Webhook signatures, Short-lived credentials, IP allowlisting, Integration monitoring - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-020** - External Integrations - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Integration tokens, API keys, or credentials logged in plain-text (application logs, access logs, stack traces) or included in notifications (Slack/Teams) leading to compromise of third-party services. - **Mitigation Strategy:** Redact secrets from logs and notifications, store tokens encrypted (KMS), rotate integration tokens frequently, implement scoped tokens with minimal privileges, and apply strict logging policies to avoid sensitive data in outputs. - **Controls Applied:** Secret redaction, KMS for tokens, Scoped tokens, Token rotation - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-022** - Repository Integration (OAuth) - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** OAuth application granted excessive scopes or abused consent to gain broad repository access; attacker leverages app to exfiltrate code or create/merge PRs. - **Mitigation Strategy:** Require least-privilege scopes for OAuth apps, implement granular scope review and just-in-time elevation for higher scopes, show clear consent screens, use fine-grained tokens, and implement periodic automatic token and scope reviews. - **Controls Applied:** Scoped OAuth tokens, Periodic scope review, Least privilege - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-023** - CI/CD Integrations - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Malicious pipeline step or compromised CI runner introduced via a PR allows execution of malicious code with elevated privileges (e.g., pushing to production or accessing secrets), especially when automatic merges are permitted. - **Mitigation Strategy:** Enforce pipeline protection (trusted runners), require CI job signatures or provenance, restrict secrets to ephemeral tokens injected only at runtime, require code reviews for pipeline changes, and avoid automatic merge-to-main without approvals. - **Controls Applied:** Trusted CI runners, Secret injection at runtime, Pipeline change reviews, Signed CI artifacts - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-026** - Data Handling / Caching - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Cache poisoning or tenant key collisions cause cached analysis results, diffs, or findings to be returned to the wrong tenant or user, exposing proprietary code or vulnerability data. - **Mitigation Strategy:** Enforce tenant-scoped cache keys, encrypt cache values where sensitive, set conservative TTLs for cached code/artifacts, isolate caches per tenant when feasible, and validate cache hit tenant context before returning results. - **Controls Applied:** Tenant-scoped cache keys, Cache encryption, TTL management - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-027** - Application Services / APIs - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Classic injection attacks (SQL injection, command injection) or unsafe deserialization in API endpoints exposing back-end data stores or enabling remote code execution within application services. - **Mitigation Strategy:** Use parameterized queries/ORMs, strict input validation and allow-lists, safe deserialization libraries, WAF protections, regular dependency vulnerability management, and secure coding practices plus SAST/DAST testing in CI. - **Controls Applied:** Parameterized queries, Input validation, WAF, SAST/DAST - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-028** - Edge/API Gateway - **Category:** Elevation of Privilege - **Likelihood:** Low | **Impact:** High - **Initial Risk Level:** High - **Description:** Misconfiguration in gateway RBAC or routing allows privilege escalation to administrative APIs (e.g., a normal user can access management endpoints or modify ACLs). - **Mitigation Strategy:** Harden gateway admin endpoints behind separate network controls and MFA, apply strict RBAC/ABAC for management APIs, separate admin plane from data plane, perform regular configuration audits, and use automated policy-as-code tests on deployments. - **Controls Applied:** Admin plane isolation, RBAC, MFA, Configuration audits - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review ##### Medium Risk Threats **THR-005** - Frontend Layer - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating PRs with suggested fixes) performed by authenticated users without their knowledge. - **Mitigation Strategy:** Enforce anti-CSRF tokens or same-site cookies for state-changing operations, require re-authentication/MFA for critical actions, validate Origin/Referer, and use strict CORS policies. - **Controls Applied:** CSRF tokens, SameSite cookies, CORS validation - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-007** - Application Services - **Category:** Repudiation - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who initiated a scan, who created/approved a PR), complicating incident response and compliance. - **Mitigation Strategy:** Generate immutable, tamper-evident audit logs with strong timestamps, forward logs to a hardened SIEM, implement write-once storage for critical logs, cryptographically sign important events, and implement log retention and access controls. - **Controls Applied:** Immutable logs (WORM), SIEM integration, Signed audit events - **Control Effectiveness:** Medium - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-013** - AI Analysis Engine / Vulnerability Feeds - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability metadata causing false positives/negatives or incorrect remediation advice. - **Mitigation Strategy:** Use signed/verifiable CVE feeds, cross-validate vulnerabilities from multiple trusted sources, implement caching with integrity verification, and maintain an internal vulnerability DB with human review for high-impact items. - **Controls Applied:** Signed feeds, Feed integrity checks, Cross-validation - **Control Effectiveness:** Medium - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-021** - External Integrations - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Flooding of webhook endpoints or continuous integration triggers to consume resources (e.g., force repeated scanning or push heavy workloads) causing degraded service. - **Mitigation Strategy:** Implement webhook rate limiting, signature verification, debounce/aggregation for repeated events, and require challenge-response during initial webhook registration. Provide per-tenant quotas. - **Controls Applied:** Rate limiting, Webhook signatures, Event debouncing - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-024** - Compliance and Reporting - **Category:** Repudiation - **Likelihood:** Low | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Tampering with compliance reports or trend analysis dashboards (intentional or accidental) to hide vulnerabilities, creating false compliance posture. - **Mitigation Strategy:** Cryptographically sign and timestamp exported reports, maintain immutable archived copies for audits, enforce access controls for report generation and modification, and monitor report generation and download events. - **Controls Applied:** Signed reports, Immutable archives, Access controls - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-025** - Team Collaboration - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Comment threads or dashboards inadvertently exposing sensitive code, PII, or secrets to unauthorized team members or external collaborators. - **Mitigation Strategy:** Enforce RBAC for comment visibility, auto-redact detected secrets and sensitive code snippets in comments, allow configurable visibility (private/public), and provide data minimization by default. - **Controls Applied:** RBAC for comments, Auto-redaction, Visibility controls - **Control Effectiveness:** Medium - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **Risk Reduction Summary:** - **Critical Risk Reduction:** 2 threats reduced from Critical to lower levels - **High Risk Reduction:** 20 threats reduced from High to lower levels - **Residual Risk Distribution:** 1 threats remain at Critical/High level ### 5.3. Risk Summary The highest-severity threats center around authentication/token spoofing, sensitive-data exposure to external model hosts, tampering with model/training artifacts, and broken access controls enabling unauthorized reads or actions. Critical entry points are the Edge/API Gateway (OAuth/JWT handling, webhooks), AI Analysis Engine/model management (external model hosts, training pipelines, model registry), and Data Storage (backups, caches, secret storage). Attack vectors include stolen/over-privileged OAuth tokens, forged webhooks, model prompt injection or poisoning, misconfigured storage exposing code/artifacts, and abuse of automated remediation (automatic PRs/merges). Overall posture is high-risk in areas where sensitive code and secrets intersect with external services and where automation can change code or permissions without enforced human oversight. Priority controls: (1) Harden authentication and token management at the gateway (short-lived tokens, mTLS, JWT validation); (2) enforce strict multi-tenant isolation and encryption for code artifacts and model training data (BYOK/KMS, private model hosting); (3) add verification and human-in-the-loop gates for automatic remediation and high-impact actions; (4) secure the model management pipeline (artifact signing, RBAC, integrity checks); (5) apply robust logging/audit immutability and SIEM monitoring; and (6) implement rate limiting, quotas and WAF/DDoS protections. Immediate attention should focus on token and webhook security, protections around model inference/training data, and preventing automatic application of untrusted fixes. Regular security testing (SAST/DAST, adversarial model testing), configuration audits, and a strong incident response plan are recommended to reduce residual risk. --- ## 6. Multi-Standard Security Requirements Mapping This section maps each functional requirement to specific security controls from multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. This multi-standard approach provides comprehensive coverage across application-level, enterprise-level, and organizational-level security domains: - **OWASP ASVS**: Application-level security controls (code, APIs, authentication, session management) - **NIST SP 800-53**: Enterprise security controls (governance, risk management, incident response) - **ISO 27001**: Information security management controls (policies, procedures, organizational controls) Requirements are prioritized based on risk assessment and compliance needs, with controls selected from the most appropriate standard(s) for each requirement type. ### 6.1. Recommended ASVS Compliance Level **Recommended Level:** L3 **Level 3: Advanced** Required for high-security applications with stringent protection requirements. Includes advanced security controls, detailed verification procedures, and enhanced threat resistance. Suitable for applications handling highly sensitive data (e.g., financial, healthcare, government) or operating in high-risk environments. The recommendation considers factors such as: - Data sensitivity and classification levels - Regulatory and compliance requirements (GDPR, HIPAA, PCI-DSS, etc.) - Threat landscape and risk assessment from threat modeling - Business criticality and potential impact of security incidents All security controls referenced in this document align with this recommended compliance level. ### 6.2. Requirements Mapping This section maps each high-level requirement to specific security controls from multiple standards (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed descriptions, relevance explanations, and integration guidance. Controls are grouped by standard for clarity. #### 6.2.1. REQ-001: Repository integration with GitHub, GitLab, and Bitbucket (OAuth + webhooks) and support for real-time commit monitoring ##### OWASP ASVS Controls **ASVS 2.3.1** **Requirement:** Ensure secure single sign-on, federated authentication and OAuth integration following best practices; validate token handling and scopes. **Relevance:** OAuth and SSO are core to integrating with external repository providers; this control ensures tokens and OAuth flows are implemented securely which directly applies to repository integration. It also highlights validating scopes to limit access from integrations and webhook handlers. **Integration Tips:** Use proven OAuth libraries, validate and enforce minimal scopes, rotate and store tokens securely, and validate webhook payload signatures. Implement token scope reviews and periodic audits of authorized integrations. **Verification Method:** Review OAuth implementation code/config; inspect scope requested during OAuth flows; verify webhook signature verification and token storage protections during pen test and code review. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AC-3** **Requirement:** The information system enforces approved authorizations for controlling access to the system and its resources in accordance with applicable access control policies. **Relevance:** Repository access via OAuth/webhooks must enforce authorization policies so only permitted repositories and users can be accessed; AC-3 maps to enforcing those approvals. **Integration Tips:** Enforce access control checks server-side for actions initiated by repo integrations; map repository identities to internal accounts and roles and validate permissions before performing actions. **Verification Method:** Inspect access control logic; run tests that attempt unauthorized repo access; review logs confirming enforcement of authorization decisions. **Priority:** Critical ##### ISO 27001:2022 Controls **A.9.4.2** **Requirement:** Where required by the access control policy, secure log-on procedures shall be implemented to confirm the identity of users. **Relevance:** OAuth login flows and automated repo integrations require secure authentication and login procedures to confirm identity before granting access to repositories or enabling webhooks. **Integration Tips:** Document and enforce secure log-on requirements for users connecting repositories; implement multi-factor authentication where appropriate and secure OAuth redirection endpoints. **Verification Method:** Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens. **Priority:** High #### 6.2.2. REQ-002: Multi-language source code analysis (Python, Java, JavaScript, Go, etc.) ##### OWASP ASVS Controls **ASVS 8.1.1** **Requirement:** Perform architecture and design level analysis and include language-specific security considerations for all supported languages. **Relevance:** Multi-language analysis requires considering language-specific threats and patterns; this control mandates language-aware threat modeling and design that matches the requirement. **Integration Tips:** Develop language-specific analysis rulesets and threat models; include language experts when defining detection heuristics and tailor scanners to idioms of each language. **Verification Method:** Review threat models and rulesets for each supported language; test scanners against seeded test cases covering language-specific issues. **Level:** L2 | **Priority:** High **ASVS 14.1.1** **Requirement:** Source code should be reviewed by automated tools and manually to detect flaws across supported languages. **Relevance:** Specifies using both automated and manual reviews across languages to detect issues tools may miss, addressing multi-language analysis completeness. **Integration Tips:** Combine static analysis with manual review workflows; ensure analysis rules cover language-specific anti-patterns and that findings are validated by security reviewers. **Verification Method:** Audit sampled findings, review false-positive rates per language, and confirm manual review steps exist in process documentation. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **SA-11** **Requirement:** The organization requires developers to employ security testing tools and techniques appropriate to the technology and languages used. **Relevance:** Mandates using appropriate security testing for each language, matching multi-language analysis requirement to use correct tooling and coverage. **Integration Tips:** Integrate language-specific static analyzers and linters into the analysis pipeline and CI; validate tool outputs and tune rules per language. **Verification Method:** Verify tool inventory and configurations include language-specific scanners; run comparative analysis with benchmark projects. **Priority:** High #### 6.2.3. REQ-003: LLM-based code review engine (inference API) with fine-tuned models and model versioning ##### OWASP ASVS Controls **ASVS 12.1.1** **Requirement:** Ensure secure design and versioning for APIs and that API endpoints enforce authentication, authorization and logging. **Relevance:** Inference APIs must implement secure API practices including authN/Z, versioning in endpoints, and logging to meet this control. **Integration Tips:** Apply token-based auth, API keys with scoped permissions, request/response logging with redaction, and explicit versioning strategies for endpoints. **Verification Method:** Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SA-15** **Requirement:** The organization requires that development processes and tools support secure design, testing, and versioning of components and models used in systems. **Relevance:** Directly maps to managing model lifecycle, versioning, and secure development practices for an LLM-based inference API and fine-tuning processes. **Integration Tips:** Implement model CI/CD with version control for model artifacts, access controls for training and inference endpoints, and documented secure development lifecycle for models. **Verification Method:** Review model versioning logs, CI/CD pipelines for model deployment, and security gating on model promotion to production. **Priority:** Critical ##### ISO 27001:2022 Controls **A.12.5.1** **Requirement:** Changes to organization assets and services should be controlled and managed including versioning and rollback procedures. **Relevance:** Applies to model versioning and controlled deployment of inference endpoints with ability to roll back if a model introduces vulnerabilities or unsafe behavior. **Integration Tips:** Use structured change management for model updates, require approvals, keep roll-back images, and maintain audit trails for model changes. **Verification Method:** Inspect change tickets for model deployments, verify rollback procedures, and confirm version metadata is immutable and auditable. **Priority:** High #### 6.2.4. REQ-004: Static code analysis and pattern recognition for security anti-patterns ##### OWASP ASVS Controls **ASVS 14.2.1** **Requirement:** Use automated static analysis tools to detect common security weaknesses and anti-patterns in source code. **Relevance:** This control explicitly requires automated static analysis to find anti-patterns, matching the requirement. **Integration Tips:** Deploy a static analysis pipeline with language-specific rules, tune detectors to reduce false positives, and integrate with developer workflows for remediation. **Verification Method:** Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **RA-5** **Requirement:** The organization scans for vulnerabilities and identifies vulnerabilities in systems and applications using automated tools. **Relevance:** Supports requirement to scan code for vulnerabilities and anti-patterns using automated tooling. **Integration Tips:** Schedule regular scans, include scans in CI, and create processes to triage and remediate findings discovered by static analysis. **Verification Method:** Verify scheduled scans run; check vulnerability tickets and remediation timelines; validate coverage of scanning tools. **Priority:** High #### 6.2.5. REQ-005: Dependency scanning for known CVEs and supply-chain vulnerabilities ##### OWASP ASVS Controls **ASVS 14.4.1** **Requirement:** Verify and manage third-party components and dependencies; ensure known vulnerabilities are tracked and remediated. **Relevance:** Specifies verifying third-party components and tracking vulnerabilities, matching dependency scanning needs. **Integration Tips:** Automate dependency scanning in CI, maintain a component inventory, and apply policies for allowed components and remediation SLAs. **Verification Method:** Audit dependency scan reports, confirm mapping to CVE/NVD, and verify remediation tickets exist for critical findings. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SA-12** **Requirement:** The organization manages supply chain risks and implements protections against supply chain vulnerabilities. **Relevance:** Directly relevant: dependency scanning and managing third-party components is a supply chain protection activity. **Integration Tips:** Ingest CVE/NVD feeds, maintain SBOMs, enforce policy gates for vulnerable dependencies and automate patch and update suggestions. **Verification Method:** Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI. **Priority:** Critical ##### ISO 27001:2022 Controls **A.15.1.1** **Requirement:** Organizations should identify and manage security risks associated with supplier relationships and third-party components. **Relevance:** Dependency scanning is a control to manage supply chain risks and aligns with supplier relationship policies. **Integration Tips:** Include third-party component checks in supplier assessments and contractual obligations; require notification of vulnerabilities from suppliers. **Verification Method:** Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans. **Priority:** High #### 6.2.6. REQ-006: Secret detection in code and configuration files ##### OWASP ASVS Controls **ASVS 14.3.1** **Requirement:** Credentials and secrets must not be hard-coded; repositories must be scanned to detect accidental leakage of secrets. **Relevance:** Directly applicable: requires detection of secrets in repos and preventing hard-coded credentials. **Integration Tips:** Integrate secret scanning in pre-commit hooks and CI, block commits with detected secrets, and provide secure secret storage alternatives (vaults). **Verification Method:** Run secret scanning on historical repositories, validate detection of seeded secrets, and confirm blocking policy enforcement. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SC-12** **Requirement:** Cryptographic keys and secrets must be managed securely and protected from unauthorized disclosure. **Relevance:** Ensures that secrets detected are not stored insecurely and that key management practices protect secrets found in code/configs. **Integration Tips:** Route detected secrets into a secure key management lifecycle, rotate exposed credentials immediately, and require replacement workflows. **Verification Method:** Inspect KM systems, verify rotation and revocation procedures were executed for exposed secrets, and review access controls to secrets. **Priority:** Critical ##### ISO 27001:2022 Controls **A.10.1.1** **Requirement:** A policy on the use of cryptographic controls should be developed and implemented to protect the confidentiality, integrity and availability of information. **Relevance:** Supports secret protection by ensuring cryptographic controls and policy guide handling of secrets discovered during scans. **Integration Tips:** Define policies requiring use of vaults, encryption for stored secrets, and handling procedures for detected secrets in code repositories. **Verification Method:** Review policy documents, confirm applied cryptographic controls, and inspect evidence of secret handling procedures. **Priority:** High #### 6.2.7. REQ-007: AI-generated remediation suggestions including code snippets and contextual explanations ##### OWASP ASVS Controls **ASVS 12.1.1** **Requirement:** Ensure APIs delivering code suggestions enforce authentication, authorization, and input/output validation. **Relevance:** APIs delivering remediation must be secured to avoid misuse and must validate both inputs (code contexts) and outputs (prevent leaking secrets). **Integration Tips:** Enforce authN/Z on suggestion endpoints, sanitize outputs to avoid leaking sensitive data, and log suggestion generation events for audit. **Verification Method:** Pen-test suggestion APIs, inspect redaction logic for outputs, and verify auth and logging configuration. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **PL-4** **Requirement:** Define acceptable use and rules for automated tools and AI, specifying responsibilities and guidance for generated content. **Relevance:** AI-generated remediation must be governed by rules of behavior to ensure safe and appropriate usage and to manage responsibilities for suggested changes. **Integration Tips:** Publish guidance for accepting AI suggestions, require human review for high-impact fixes, and document responsibilities for code changes based on AI outputs. **Verification Method:** Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions. **Priority:** High ##### ISO 27001:2022 Controls **A.14.2.3** **Requirement:** Protection of data used for testing and development including sanitized code samples and generated outputs. **Relevance:** AI-generated snippets must be handled as potentially sensitive test/development data; this control ensures outputs are sanitized and handled per policy. **Integration Tips:** Sanitize training/test data, strip secrets from generated snippets, and apply classification/handling rules to AI outputs before display or storage. **Verification Method:** Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage. **Priority:** High #### 6.2.8. REQ-008: Automatic pull request creation and optional auto-apply fixes with approval workflows ##### OWASP ASVS Controls **ASVS 14.1.1** **Requirement:** Source code changes should be subject to review and must be tracked by secure processes including merge/PR workflows. **Relevance:** Specifies that code changes (including automated ones) must be tracked and reviewed, aligning with the PR and approval workflow requirement. **Integration Tips:** Ensure automated PRs include evidence of the vulnerability, suggested changes, and require at least one human reviewer before merge for sensitive fixes. **Verification Method:** Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **CM-3** **Requirement:** Changes to system configuration and software must be controlled, including approval and authorization prior to implementation. **Relevance:** Auto-apply fixes and PR creation must be subject to change control and approvals to avoid unauthorized or unsafe automatic changes. **Integration Tips:** Implement approval gates, require signed commits for auto-applied changes, and log automated change proposals with audit trails. **Verification Method:** Verify workflow enforces approvals, review automated PRs created and ensure a rollback path exists with audit logs. **Priority:** Critical ##### ISO 27001:2022 Controls **A.12.1.2** **Requirement:** Changes to information processing facilities and systems should be managed following formal change control processes. **Relevance:** Auto-apply fixes must adhere to formal change management to ensure integrity and accountability of deployments. **Integration Tips:** Tie auto-PRs to change tickets, require approvals for auto-apply in production, and maintain records of applied changes and approvals. **Verification Method:** Review change records for auto-applied fixes, confirm approval traces, and test rollback processes. **Priority:** High #### 6.2.9. REQ-009: Severity scoring, risk assessment, and contextual prioritization ##### OWASP ASVS Controls **ASVS 14.4.1** **Requirement:** Vulnerabilities should be prioritized by severity and context to guide remediation efforts. **Relevance:** Directly supports contextual prioritization and severity scoring of vulnerabilities identified by the platform. **Integration Tips:** Map scanner findings to severity taxonomies, incorporate context like code exposure and exploitability, and surface prioritized lists to developers. **Verification Method:** Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **RA-3** **Requirement:** Conduct risk assessments to identify threats and vulnerabilities and determine risk levels for organizational operations and assets. **Relevance:** Provides framework for scoring and assessing the risk of findings, which is necessary for prioritization and remediation planning. **Integration Tips:** Implement contextual scoring models that consider asset criticality, exploitability, and business impact; align scoring with organizational risk tolerance. **Verification Method:** Review risk assessment methodology, sample scored findings and ensure scoring matches defined criteria and business context. **Priority:** Critical ##### ISO 27001:2022 Controls **A.8.2.1** **Requirement:** Information should be classified to support risk-based handling and prioritization. **Relevance:** Classification of assets and codebases supports contextual prioritization by indicating sensitivity and business impact. **Integration Tips:** Classify repositories and modules by criticality and exposure, and use classification to weight severity scoring for findings. **Verification Method:** Check classification registers and verify scoring integrates classification attributes into prioritization. **Priority:** Medium #### 6.2.10. REQ-010: Developer dashboards with personalized security metrics and trend analysis ##### OWASP ASVS Controls **ASVS 12.1.1** **Requirement:** APIs providing dashboards must enforce authentication, authorization, and secure data presentation. **Relevance:** Dashboards expose sensitive security metrics per developer; API and UI must enforce access controls and secure handling of metrics. **Integration Tips:** Implement per-user RBAC, restrict metric visibility to permitted scopes, and sanitize any user-provided inputs used in dashboard queries. **Verification Method:** Test API endpoints for access control bypass, review role mappings, and inspect UI rendering for sensitive data leaks. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **AU-6** **Requirement:** Review and analyze audit records and report findings to support security monitoring and metrics. **Relevance:** Dashboards should consume audited events and present trend analysis; AU-6 maps to creating reliable metrics from logs. **Integration Tips:** Feed audited security events into metrics pipelines, ensure timestamps and user context are preserved, and implement access controls on metrics. **Verification Method:** Compare dashboard metrics against raw audit logs to ensure accuracy and completeness. **Priority:** Medium ##### ISO 27001:2022 Controls **A.7.2.2** **Requirement:** Employees should receive appropriate awareness and training to support information security responsibilities, enabling personalized metrics to drive improvements. **Relevance:** Personalized metrics can be used to drive training and awareness; this control supports using dashboards for developer improvement. **Integration Tips:** Map dashboard insights to training campaigns and provide links to remediation resources; ensure privacy-considerate presentation of developer metrics. **Verification Method:** Verify links to training are available and that metrics are used in awareness programs. **Priority:** Low #### 6.2.11. REQ-011: Issue comment threads, collaboration features, and integration with Slack/Teams notifications ##### OWASP ASVS Controls **ASVS 12.1.1** **Requirement:** External integrations (e.g., Slack, Teams) must be authenticated and use secure channels. **Relevance:** Notifications and comment integrations must be securely authenticated and ensure messages don't leak sensitive information. **Integration Tips:** Use application-level tokens with minimum scopes for integrations, encrypt webhook payloads, and redact sensitive fields before sending notifications. **Verification Method:** Inspect integration tokens and scopes, verify payload encryption/signing, and test for sensitive data in notifications. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **AU-2** **Requirement:** The information system provides audit records for defined events to support incident notification and collaboration. **Relevance:** Issue threads and notifications generate events that must be auditable for traceability and incident investigations. **Integration Tips:** Log all collaboration events, map notification events to findings, and include correlation IDs to tie notifications back to findings. **Verification Method:** Review audit logs for collaboration events and confirm correlation between notifications and logged events. **Priority:** Medium ##### ISO 27001:2022 Controls **A.13.2.1** **Requirement:** Policies and procedures should be established for information transfer including notifications and collaboration channels. **Relevance:** Provides policy-level guidance for handling notifications and collaboration channels to avoid inadvertent data exposure. **Integration Tips:** Define acceptable messaging content, retention, and redaction rules for notifications; require approval for sending high-sensitivity data through channels. **Verification Method:** Review information transfer policies and sample notification content for compliance with policies. **Priority:** Medium #### 6.2.12. REQ-012: Integration with CI/CD pipelines for pre-merge and post-merge scanning ##### OWASP ASVS Controls **ASVS 14.1.1** **Requirement:** Incorporate automated security testing into the development lifecycle and CI/CD pipelines. **Relevance:** Specifies integrated security testing in CI/CD, covering pre-merge and post-merge scanning requirements. **Integration Tips:** Embed static analysis, dependency scanning, and secret detection as pipeline stages and fail builds on critical findings; provide feedback in PR checks. **Verification Method:** Review CI pipeline configs, confirm scans run on pre-merge and post-merge events, and validate PR blocking behavior for critical issues. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SA-11** **Requirement:** Require developer security testing and evaluation, including integration into build and CI/CD pipelines. **Relevance:** Supports requirement to enforce security testing as part of CI/CD and developer workflows. **Integration Tips:** Mandate pipeline gating for security checks, store results in centralized dashboards, and require remediation before merge for high-severity issues. **Verification Method:** Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement. **Priority:** High ##### ISO 27001:2022 Controls **A.14.2.1** **Requirement:** Establish secure development policies including integration of security testing into development processes and pipelines. **Relevance:** Policy-level requirement mandating inclusion of security in development that informs CI/CD integration choices. **Integration Tips:** Document secure development requirements and enforce them via pipeline checks; require exception handling and approval for bypasses. **Verification Method:** Review secure development policy and pipeline configurations for evidence of enforcement. **Priority:** Medium #### 6.2.13. REQ-013: OWASP Top 10 and custom security policy enforcement with customizable rules ##### OWASP ASVS Controls **ASVS 14.4.1** **Requirement:** Enforce security policies including OWASP Top 10 and provide mechanisms for custom rules and policy enforcement. **Relevance:** Directly mandates policy enforcement including OWASP Top 10 and extensibility for custom rules aligning with the requirement. **Integration Tips:** Provide configuration UI for custom rules, map findings to OWASP Top 10 categories, and allow policy-driven blocking or advisory actions. **Verification Method:** Test custom rule creation, verify policy mapping to OWASP categories, and confirm enforcement in scans and gates. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **PL-2** **Requirement:** Develop and disseminate system and communications protection policies and procedures tailored to system needs. **Relevance:** Supports the existence of policies and procedures that define the security rules to be enforced by the platform. **Integration Tips:** Document OWASP Top 10 mapping and custom rule lifecycle; communicate policy changes to users and enforce them via automated tooling. **Verification Method:** Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated. **Priority:** High ##### ISO 27001:2022 Controls **A.5.1.1** **Requirement:** Management should define a set of information security policies to provide management direction and support. **Relevance:** Requires management-level approval for security policies which underlie enforcement of OWASP Top 10 and custom rules. **Integration Tips:** Obtain policy approval for enforced rules, ensure responsibilities for rule management are assigned, and align with compliance requirements. **Verification Method:** Check for signed policy documents and traceability between policies and enforcement mechanisms. **Priority:** Medium #### 6.2.14. REQ-014: Audit logging and immutable records of findings, decisions, and remediation history ##### OWASP ASVS Controls **ASVS 12.3.1** **Requirement:** Ensure that security-relevant events are logged with integrity and protected from tampering; maintain sufficient detail for investigations. **Relevance:** Specifies log integrity and tamper protection necessary to maintain immutable records of findings and remediation history. **Integration Tips:** Use append-only logs, cryptographic signing of log entries or WORM storage, and ensure access controls to prevent log modification. **Verification Method:** Verify cryptographic log integrity, attempt tamper tests in controlled env, and review log access controls. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AU-2** **Requirement:** The information system provides audit records for the organization-defined events to support after-action review and investigations. **Relevance:** Auditing findings, decisions, and remediation actions requires defined audit events and logs as per AU-2. **Integration Tips:** Define audit event taxonomy for findings lifecycle events, ensure timestamps and user IDs are captured, and route logs to a tamper-resistant store. **Verification Method:** Review audit event definitions, sample logs for required events, and validate retention and integrity protections. **Priority:** Critical **AU-9** **Requirement:** Protect audit records from unauthorized access, modification, and deletion to preserve integrity of logs. **Relevance:** Further enforces protection for audit records to ensure they remain immutable per requirement. **Integration Tips:** Segment log storage, implement RBAC for log access, and use encryption and integrity checks for stored logs. **Verification Method:** Review access lists for log storage, check encryption keys usage, and validate log retention policies. **Priority:** High #### 6.2.15. REQ-015: Exportable compliance reports and evidence packages ##### OWASP ASVS Controls **ASVS 12.1.1** **Requirement:** APIs that export reports must enforce access controls and ensure integrity of exported evidence. **Relevance:** Export APIs must be protected to prevent unauthorized access or tampering with exported compliance evidence. **Integration Tips:** Require authenticated, authorized requests for exports, log export events, and sign exported packages using organizational keys. **Verification Method:** Test export APIs for access control enforcement and verify signatures on exported packages. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **CA-7** **Requirement:** Establish continuous monitoring processes to produce evidence and reports of system security status. **Relevance:** Continuous monitoring provides the underlying data necessary for exportable compliance reports and evidence packaging. **Integration Tips:** Implement scheduled evidence exports, preserve raw logs for audits, and provide signed reports to demonstrate unaltered evidence. **Verification Method:** Validate that continuous monitoring feeds into report generation and that exported reports contain verifiable signatures. **Priority:** Medium ##### ISO 27001:2022 Controls **A.18.2.3** **Requirement:** Information security should be reviewed regularly and results available to demonstrate compliance. **Relevance:** Requires the ability to produce evidence and review results which align with generating exportable compliance reports and evidence packages. **Integration Tips:** Design reporting features to include signed evidence, timestamps, and contextual logs; map report fields to compliance requirements. **Verification Method:** Produce sample evidence packages for audits and confirm completeness against compliance checklists. **Priority:** High #### 6.2.16. REQ-016: Model management: inference API endpoints, versioning, A/B testing, performance monitoring, and drift detection ##### OWASP ASVS Controls **ASVS 12.1.1** **Requirement:** Ensure API endpoints enforce authentication, authorization, and logging to support management and auditing of model endpoints. **Relevance:** Inference endpoints must be protected and logged to support model management including versioning and performance tracking. **Integration Tips:** Include version identifiers in API paths or headers, enforce scoped access for model operations, and collect performance telemetry for drift detection. **Verification Method:** Verify authN/Z on endpoints, confirm version headers are present in logs, and review telemetry ingestion. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **SA-15** **Requirement:** The organization ensures development processes and tools support secure design, testing, versioning and management of components such as models. **Relevance:** Supports a structured approach to managing model lifecycle, versioning and testing (A/B) as part of secure development processes. **Integration Tips:** Adopt model governance frameworks, keep version metadata, manage access to model artifacts, and ensure scripted promotion paths with approvals. **Verification Method:** Review governance artifacts, model registries, A/B testing configs, and access controls for model artifacts. **Priority:** Critical **SI-4** **Requirement:** Monitor the information system to detect attacks and potential misuse, including monitoring model performance and anomalies. **Relevance:** Monitoring for drift and abnormal model behavior maps to SI-4's requirement for system monitoring. **Integration Tips:** Implement alerts for performance regressions, model output anomalies, and integrate monitoring with incident workflows for model roll-back. **Verification Method:** Review monitoring rules, simulate drift scenarios, and confirm alerts trigger defined responses. **Priority:** High #### 6.2.17. REQ-017: Custom model training on organization codebases with privacy-preserving controls ##### OWASP ASVS Controls **ASVS 14.3.1** **Requirement:** Protect sensitive data used for model training and avoid inclusion of secrets in training datasets. **Relevance:** Prevents leakage of secrets into trained models and enforces cleaning and scanning prior to using code as training data. **Integration Tips:** Scan and sanitize training corpora to remove secrets, apply redaction, and incorporate data provenance metadata to track origin and transformations. **Verification Method:** Run secret scans on training datasets, validate redaction effectiveness, and review provenance logs. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **PL-4** **Requirement:** Define acceptable use and rules for systems handling sensitive data including restrictions for training data usage. **Relevance:** Using org codebases for model training requires clear rules defining consent, allowed use, and privacy-preserving constraints. **Integration Tips:** Define acceptable-use agreements, require data minimization and anonymization before training, and log consent and provenance of training data. **Verification Method:** Inspect rules of behavior, check anonymization pipelines, and confirm consent records for training datasets. **Priority:** Critical ##### ISO 27001:2022 Controls **A.8.2.3** **Requirement:** Ensure appropriate handling of assets including data used for training, protecting confidentiality and privacy. **Relevance:** Specifies safe handling of data assets, which applies to codebases used for custom model training and privacy controls. **Integration Tips:** Implement handling procedures for training data, restrict access, and apply privacy-preserving techniques like differential privacy where required. **Verification Method:** Audit access to training datasets and review applied privacy-preserving measures and their tuning parameters. **Priority:** High #### 6.2.18. REQ-018: Secure storage and handling of code snippets, tokens, keys, and analysis results (encryption, retention, redaction) ##### OWASP ASVS Controls **ASVS 2.4.1** **Requirement:** Sensitive data at rest and in transit must be protected using strong cryptography; implement key management. **Relevance:** Specifies practical data protection measures (encryption, key management, redaction) for code snippets and tokens. **Integration Tips:** Implement field-level redaction, encrypt databases and object storage for analysis results, and store tokens in vaults not in code or plain DB fields. **Verification Method:** Inspect encryption settings, redaction logic, and confirm secrets are stored only in approved vaults with access auditing. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SC-13** **Requirement:** Employ cryptographic mechanisms to prevent unauthorized disclosure and modification of information. **Relevance:** Mandates encryption of sensitive artifacts (tokens, keys, snippets) at rest and in transit. **Integration Tips:** Encrypt data-at-rest with key management, TLS for transit, and ensure encryption keys are stored in a dedicated KMS with access controls. **Verification Method:** Verify encryption configuration, test data retrieval unauthorized attempts, and review KMS access logs. **Priority:** Critical ##### ISO 27001:2022 Controls **A.10.1.1** **Requirement:** A policy on the use of cryptographic controls should be developed and implemented; protect keys and sensitive data. **Relevance:** Requires organizational policy guiding encryption, retention and redaction of sensitive stored data consistent with the requirement. **Integration Tips:** Define retention schedules, redaction rules for stored results, and key rotation policies in alignment with the cryptography policy. **Verification Method:** Review cryptography policy and sample stored artifacts for correct encryption and redaction settings. **Priority:** High #### 6.2.19. REQ-019: Vulnerability database management and mapping to CVE/NVD feeds ##### OWASP ASVS Controls **ASVS 14.4.1** **Requirement:** Verify and manage third-party components and dependencies; ensure known vulnerabilities are tracked and remediated. **Relevance:** Direct guidance to track third-party component vulnerabilities and remediate based on feeds like CVE/NVD. **Integration Tips:** Automate periodic feed ingestion, implement matching algorithms to identify impacted versions, and assign remediation tickets. **Verification Method:** Review feed ingestion logs, confirm CVE-to-component mappings, and check remediation ticket lifecycle. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **CM-8** **Requirement:** Maintain an inventory of system components and correlate with known vulnerabilities and feeds to manage vulnerabilities. **Relevance:** Maintaining an inventory and mapping to CVE/NVD is core to managing vulnerabilities discovered via dependency scanning and component analysis. **Integration Tips:** Keep automated inventories (SBOM), ingest NVD feeds, auto-correlate component versions to CVEs, and flag impacted systems. **Verification Method:** Verify SBOM accuracy, correlation logs showing CVE matches, and check records of vulnerability notifications and remediations. **Priority:** Critical ##### ISO 27001:2022 Controls **A.12.6.1** **Requirement:** Obtain and evaluate information about technical vulnerabilities and take appropriate measures to address them. **Relevance:** Mandates operational process for vulnerability feed evaluation and measures — applies to mapping CVE/NVD into platform workflows. **Integration Tips:** Define SLAs for vulnerability evaluation, maintain a feed processing pipeline, and provide visibility to stakeholders on findings. **Verification Method:** Examine vulnerability management reports, SLA adherence, and evidence of action on identified CVEs. **Priority:** High #### 6.2.20. REQ-020: Caching of analysis results for performance with cache invalidation policies ##### OWASP ASVS Controls **ASVS 5.1.1** **Requirement:** Ensure correct state management and caching strategies that do not leak sensitive data or create stale/inaccurate results. **Relevance:** Addresses state and caching correctness to prevent stale or sensitive data being served in caches. **Integration Tips:** Separate caches by tenancy/repo scope, implement per-item TTLs, and ensure caches are invalidated on PRs or commits that change analysis context. **Verification Method:** Review cache configuration, run invalidation scenarios, and check caches for unexpectedly persisted sensitive data. **Level:** L2 | **Priority:** Medium ##### NIST SP 800-53 Controls **SC-5** **Requirement:** Protect system availability including caching mechanisms that may affect availability and implement controls to mitigate DoS and stale data. **Relevance:** Caching can affect availability and stale results; SC-5 maps to protecting availability and ensuring cache policies don't lead to DoS or stale/inaccurate findings. **Integration Tips:** Implement cache eviction, TTL, and capacity controls; protect cache endpoints from abuse and ensure invalidation on repository changes. **Verification Method:** Test cache invalidation on commits, simulate heavy load and confirm DoS protections and cache limits. **Priority:** Medium **CM-6** **Requirement:** Establish and document baseline configurations and settings for caching behaviors including invalidation policies. **Relevance:** Requires documented config and baseline for caching behavior which helps ensure consistent invalidation policies and performance tuning. **Integration Tips:** Document cache baselines, include recommended TTLs, and ensure configuration is part of deployment manifests with change control. **Verification Method:** Verify documented cache baselines and inspect deployed configurations for alignment. **Priority:** Low #### 6.2.21. REQ-021: Access control and RBAC for repo access, model access, and UI/API operations ##### OWASP ASVS Controls **ASVS 2.2.1** **Requirement:** Implement robust authentication and session controls and role-based access control for applications and APIs. **Relevance:** Specifies implementing RBAC for application/API sessions to control access to sensitive operations like model use or repo modifications. **Integration Tips:** Define roles for repo admins, reviewers, model trainers; implement session timeouts, consent-based scopes, and token revocation for role changes. **Verification Method:** Test role-based access enforcement for UI and APIs and verify session handling behaviors. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AC-2** **Requirement:** Manage information system accounts including provisioning, de-provisioning, and role assignments. **Relevance:** RBAC and account lifecycle management are necessary to control repo/model/UI/API access across the platform. **Integration Tips:** Implement automated provisioning/deprovisioning tied to identity sources, enforce least privilege and role reviews, and maintain account inventories. **Verification Method:** Review account provisioning logs, role assignments and deprovision records; perform access reviews. **Priority:** Critical ##### ISO 27001:2022 Controls **A.9.1.2** **Requirement:** Users should only be provided access to network services necessary to perform their role. **Relevance:** Supports least privilege and RBAC by restricting access to only necessary services and platform functions. **Integration Tips:** Segment admin functions into separate services and apply RBAC at service and API levels; enforce network segmentation where appropriate. **Verification Method:** Inspect access policies and run access tests to confirm role-appropriate service access. **Priority:** High #### 6.2.22. REQ-022: Rate limiting, abuse prevention, and API authentication/authorization ##### OWASP ASVS Controls **ASVS 12.1.1** **Requirement:** Ensure APIs enforce authentication, authorization, rate limiting and input/output validation to prevent abuse. **Relevance:** Directly applicable: enforces rate limiting and abuse prevention controls on APIs used for inference and scanning. **Integration Tips:** Implement per-API and per-user throttle limits, API keys with quotas, usage monitoring and automated throttling on anomalous behavior. **Verification Method:** Run load and abuse tests to confirm throttling, inspect auth/authorization policies and quota enforcement logs. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **IA-5** **Requirement:** Manage authenticators and credentials and protect against misuse and brute-force or credential stuffing attacks. **Relevance:** Helps prevent abuse by ensuring robust authenticator management and protections against credential-based attacks on APIs. **Integration Tips:** Enforce strong credential policies, lockout thresholds, multi-factor authentication for privileged API access, and monitor for credential misuse. **Verification Method:** Review authenticator policies, test brute-force protections and check account lockout logs. **Priority:** High ##### ISO 27001:2022 Controls **A.13.1.1** **Requirement:** Networks should be managed and controlled to protect information in systems and applications, including controls to mitigate abuse and overload. **Relevance:** Network-level controls complement API rate limiting to prevent overload and abuse impacting service availability. **Integration Tips:** Apply rate limiting at edge proxies, use WAF and DDoS protections, and configure network ACLs to mitigate abusive traffic sources. **Verification Method:** Review edge proxy configs, WAF rules, and DDoS protection telemetry during simulated abuse. **Priority:** Medium #### 6.2.23. REQ-023: Support for on-premises or isolated VPC/SaaS deployment models and data residency controls ##### OWASP ASVS Controls **ASVS 14.1.1** **Requirement:** Support secure deployment models including separated environments, hardened infrastructure and data residency controls. **Relevance:** Specifies secure deployment approaches and supports differentiating on-prem/VPC or SaaS with data residency controls. **Integration Tips:** Offer configurable deployment templates for on-prem and VPC isolate; implement data locality tagging and policies to restrict cross-border data movements. **Verification Method:** Review deployment manifests for isolation features and validate data residency enforcement in sample deployments. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **SC-7** **Requirement:** Monitor and control communications at external boundaries and implement deployment-specific network segmentation such as VPC isolation. **Relevance:** Maps to implementing VPC isolation and boundary protections for on-prem and isolated deployments. **Integration Tips:** Use network segmentation, private endpoints, and peering controls to enforce isolation; restrict management interfaces to secure management networks. **Verification Method:** Pen-test network boundaries, inspect VPC configs, and verify management plane access restrictions. **Priority:** High ##### ISO 27001:2022 Controls **A.8.1.3** **Requirement:** Define and communicate acceptable use of organizational assets including deployment choices affecting data location and handling. **Relevance:** Policy-level control to ensure data residency and deployment choices are authoritative and administratively enforced. **Integration Tips:** Provide customers with clear acceptable-use and data residency options in contracts and deployment docs; enforce via deployment automation. **Verification Method:** Review contractual terms, deployment records and customer configuration options for compliance to stated residency. **Priority:** Medium #### 6.2.24. REQ-024: Consent, data usage policies, and ability to opt-in/opt-out model training on customer code ##### OWASP ASVS Controls **ASVS 2.4.1** **Requirement:** Protect data and ensure proper handling including consent and data usage policies for training or analysis datasets. **Relevance:** Reinforces need to protect data used for training and ensure customer consent is respected and enforced. **Integration Tips:** Tag datasets with consent metadata, enforce dataset filters in training pipelines that honor opt-out tags, and provide customers audit evidence. **Verification Method:** Inspect dataset tagging and training pipelines, confirm that opt-out tags prevent inclusion in training datasets. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **PL-4** **Requirement:** Define rules and agreements for acceptable use, including consent and data usage policies for customers. **Relevance:** Supports documenting and enforcing rules of behavior and data usage policies for customers regarding model training. **Integration Tips:** Define and communicate clear opt-in/out mechanisms, include data usage terms in contracts and UI consent, and log customer preferences. **Verification Method:** Check rules of behavior documentation and verify system honors stored user preferences during training runs. **Priority:** High ##### ISO 27001:2022 Controls **A.18.1.4** **Requirement:** Ensure privacy and protection measures for PII and comply with applicable privacy laws, including consent for data use. **Relevance:** Specifies need for consent and privacy protections which directly apply to opt-in/opt-out model training decisions for customer code (which may include PII). **Integration Tips:** Implement consent capture UI, store consent metadata with datasets, and enforce opt-out by excluding datasets from training pipelines. **Verification Method:** Review consent records, confirm excluded datasets are omitted from training, and audit model training inputs for PII. **Priority:** Critical #### 6.2.25. REQ-025: Monitoring, alerting, and incident response for platform security (SIEM integration) ##### OWASP ASVS Controls **ASVS 12.3.1** **Requirement:** Ensure detection and response capabilities are in place with integrated logs and monitoring to support incident response. **Relevance:** Requires application-level logging and integration with monitoring tools to support SIEM and IR activities for the platform. **Integration Tips:** Ensure application logs include context (user, repo, model version), push logs to SIEM, and configure runbooks for prioritized alerts. **Verification Method:** Audit logs for required context fields, verify SIEM correlation rules and test full incident response flow. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **SI-4** **Requirement:** Monitor the information system to detect attacks and potential misuse and send alerts to administrators. **Relevance:** Core to SIEM integration: requires monitoring and alerting to detect anomalies, misuse, and security events in the platform. **Integration Tips:** Forward application and infrastructure logs to SIEM, implement correlation rules for key events (auth failures, suspicious model queries), and configure alerting thresholds. **Verification Method:** Validate SIEM ingests logs, simulate incidents and confirm alerts, and review incident ticket creation. **Priority:** Critical ##### ISO 27001:2022 Controls **A.16.1.5** **Requirement:** Establish responsibilities and procedures to ensure a quick, effective and orderly response to information security incidents. **Relevance:** Defines incident response processes which must be integrated with monitoring/alerting and SIEM to handle platform security events. **Integration Tips:** Document incident response playbooks for platform-specific incidents, integrate SIEM alerts into incident workflows and perform regular exercises. **Verification Method:** Review incident response plans, confirm SOPs for SIEM alerts, and evaluate results of tabletop or live exercises. **Priority:** High ### 6.3. Cross-Functional Security Controls The following controls apply globally across all system components: **Logging and Auditability** *Description:* Comprehensive logging of authentication events, API calls, model inferences, scans, PR actions, and remediation decisions with protections for integrity and retention. *Applies to:* Repository integration, LLM inference API, Static analysis, Dependency scanning, Secret detection, Auto PRs, Dashboards, Notifications, CI/CD integration, Model training, Storage *Implementation Guidance:* Implement structured, tamper-evident logs with correlation IDs, forward to centralized SIEM, protect logs with RBAC and encryption, and retain logs per compliance retention schedules. **Encryption and Key Management** *Description:* Encrypt sensitive data at rest and in transit, and manage keys using a centralized KMS with rotation and access controls. *Applies to:* Token storage, Code snippets, Model artifacts, Databases, Backups, Exported evidence *Implementation Guidance:* Use TLS for transport, AES-256 for storage, KMS-backed envelope encryption, enforce key rotation policies, and restrict KMS access via IAM roles. **Access Control and RBAC** *Description:* Centralized role-based access control for repository access, model operations, APIs and UI functions with account lifecycle management. *Applies to:* Repo access, Model management, Admin interfaces, API endpoints, Dashboards *Implementation Guidance:* Integrate with enterprise identity providers, implement least privilege roles, periodic access reviews, and automated provisioning/deprovisioning. **Data Protection and Privacy** *Description:* Policies and technical controls for data handling including consent, opt-in/opt-out for training, redaction, anonymization, and data residency enforcement. *Applies to:* Custom model training, Storage of code and snippets, Exported reports, Cross-border deployments *Implementation Guidance:* Capture consent metadata, tag datasets with residency and sensitivity, apply anonymization/differential privacy for training, and enforce opt-out filters in pipelines. ### 6.4. Requirements Traceability Overview This section demonstrates complete traceability from high-level requirements through threats to security controls and verification methods. **Coverage Summary:** Traceability matrix contains 28 requirements. 28 requirements (100.0%) linked to threats. 25 requirements (89.3%) mapped to security controls (OWASP ASVS, NIST SP 800-53, ISO 27001). Coverage: Partial. #### Sample Traceability Mappings The following table shows traceability for high-priority requirements: | Req ID | Requirement | Threats | Security Controls | Standards | Priority | Verification | |--------|-------------|---------|-------------------|-----------|----------|-------------| | REQ-001 | Repository integration with GitHub, GitL... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens. | | REQ-003 | Provide an LLM-based code review engine ... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers. | | REQ-004 | Perform static code analysis (SAST) and ... | 10 threats | 2 controls | NIST, OWASP | Critical | Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates. | | REQ-005 | Dependency scanning that identifies libr... | 9 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI. | | REQ-006 | Secret detection across code, config fil... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI. | | REQ-008 | Support automatic pull request creation ... | 7 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs. | | REQ-009 | Provide severity scoring and risk assess... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans. | | REQ-013 | Integrate with CI/CD pipelines (e.g., Gi... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement. | | REQ-015 | Provide OWASP Top 10 checks and allow cr... | 7 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated. | | REQ-016 | Maintain immutable audit logs of all ana... | 10 threats | 3 controls | NIST, OWASP | Critical | Review audit event definitions, sample logs for required events, and validate retention and integrity protections. | *Showing 10 of 28 requirements. See Appendix D for complete traceability matrix.* #### Traceability Statistics - **Total Requirements Tracked:** 28 - **Requirements Linked to Threats:** 28 (100.0%) - **Requirements Mapped to Controls:** 25 (89.3%) - **Average Controls per Requirement:** 2.6 - **Control Distribution by Standard:** - NIST SP 800-53: 27 controls - OWASP ASVS: 26 controls - ISO 27001: 21 controls - **Verification Coverage:** 100% (all requirements have verification methods) --- ## 7. AI/ML Security Requirements This section addresses security requirements specific to artificial intelligence and machine learning components within the system. AI/ML systems introduce unique security challenges including prompt injection attacks, data poisoning, model theft, adversarial inputs, and bias vulnerabilities. This analysis identifies AI/ML components, assesses their security risks, and prescribes specialized controls to protect both the AI systems themselves and the data they process. ### 7.1. AI/ML Components Detected This section identifies all AI/ML components within the system that require specialized security controls. 1. **LLM-based Code Review Engine**: Utilizes fine-tuned large language models for analyzing code commits, identifying vulnerabilities, and providing remediation suggestions. 2. **Static Code Analysis Engine**: Employs machine learning techniques for vulnerability detection and pattern recognition, including dependency scanning for known CVEs. 3. **AI-generated Remediation Suggestions**: Provides contextual explanations and code snippets for identified vulnerabilities, utilizing AI to enhance developer understanding and actionability. 4. **Model Management System**: Responsible for managing model inference, versioning, and custom training based on organization-specific codebases. ### 7.2. AI/ML Threat Model | Component | Identified Threats | |------------------------------------|--------------------------------------------------------| | LLM-based Code Review Engine | - Prompt injection | | | - Data leakage through training data | | | - Adversarial inputs | | Static Code Analysis Engine | - Model poisoning | | | - Data leakage of PII and sensitive information | | AI-generated Remediation Suggestions | - Output filtering vulnerabilities | | | - Misleading or incorrect suggestions | | Model Management System | - Model inversion attacks | | | - Supply chain vulnerabilities | ### 7.3. AI/ML Security Controls #### LLM-based Code Review Engine **Prompt Injection Prevention**: Implement strict input validation for all prompts sent to the model. Use whitelists for acceptable input patterns. **Output Filtering and Sanitization**: Apply filtering mechanisms to sanitize outputs before displaying them to users, ensuring that potentially harmful content is removed. #### Static Code Analysis Engine **Input Validation for AI Inputs**: Enforce rigorous validation on all inputs to the static analysis engine to prevent injection attacks and malicious inputs. **Data Leakage Prevention**: Ensure that proprietary or sensitive code is never included in training data. Use techniques like data anonymization. #### AI-generated Remediation Suggestions **Contextual Output Filtering**: Implement logic to ensure AI-generated suggestions are contextually relevant and accurate, reducing the risk of misleading fixes. **Model Access Controls**: Enforce strict access controls to limit who can access and utilize the model for generating suggestions, preventing misuse. #### Model Management System **Model Versioning and Rollback Capabilities**: Maintain a comprehensive versioning system that allows for rollback to previous model versions in case of detected issues or vulnerabilities. **Supply Chain Security for Models**: Implement checks and integrity verification for all models sourced externally to ensure they have not been tampered with. ### 7.4. Integration with Existing Security Controls AI controls should be integrated into existing security practices such as the OWASP Top 10 compliance checks, ensuring that all AI-related components are assessed for vulnerabilities. Additionally, the use of standard authentication and access control measures (like OAuth and RBAC) can complement AI-specific controls to create a more robust security posture. ### 7.5. AI/ML Monitoring Requirements | Monitoring Area | Description | |-------------------------------------|---------------------------------------------------------| | Prompt Injection Monitoring | Continuously monitor inputs for signs of prompt injection attempts. | | Adversarial Input Monitoring | Implement detection systems for unusual patterns that indicate adversarial attacks on the AI model. | | Model Performance Monitoring | Regularly assess model outputs for drift and degradation in performance. | | Data Leakage Monitoring | Monitor logs for unauthorized access attempts to sensitive data, including code snippets and tokens. | --- ## 8. Compliance Requirements This section identifies regulatory and legal compliance obligations applicable to the system based on data types, geographic scope, industry sector, and business operations. Compliance requirements drive specific security controls, data handling procedures, audit capabilities, and privacy protections. Non-compliance can result in significant legal penalties, reputational damage, and business disruption. This analysis maps applicable regulations to specific security requirements and operational procedures. ### 8.1. Applicable Regulations The AI-powered code review and security analysis platform processes various categories of sensitive data, including source code, authentication tokens, and potential proprietary information. The regulations identified stem from the types of data handled, the geographic scope of operations, and the nature of the industry, particularly focusing on data protection and security compliance. Compliance requirements directly impact security controls, data handling procedures, and operational processes to ensure that the platform adheres to legal obligations. The following table outlines the applicable regulations and the reasons for their applicability: | Regulation | Applicability Reason | |-------------|-----------------------| | GDPR | Applies because the system processes personal data of EU residents, which includes any identifiable information. | | CCPA | Applies as the platform may collect and process personal data of California residents, providing rights related to their data. | | HIPAA | Relevant if health-related information is processed, particularly if the code includes health-related data or services. | | PCI-DSS | Applicable if payment card information is processed or stored in the platform, including tokens and API keys. | | SOX | Relevant for financial data handling and ensuring the integrity of financial reporting and audit trails. | | COPPA | Applies if the platform collects data from children under 13, necessitating parental consent. | | Data Residency Laws | Relevant if data is stored or processed in specific jurisdictions that impose restrictions on data movement. | ### 8.2. Compliance Controls by Regulation #### GDPR - **Data Minimization:** Ensure only necessary data is processed. - **Access Controls:** Implement strict role-based access controls (RBAC) to limit data access. - **Data Encryption:** Encrypt personal data at rest and in transit. - **Privacy by Design:** Integrate privacy considerations in the development process. #### CCPA - **Opt-Out Mechanism:** Provide users the ability to opt-out of data selling. - **Disclosure Requirements:** Inform users about data collection and usage practices. - **Access Requests:** Facilitate user requests for data access and deletion. #### HIPAA - **PHI Protection:** Implement safeguards to protect personally identifiable health information. - **Business Associate Agreements:** Ensure contracts with third-party vendors handling PHI include HIPAA compliance clauses. - **Training:** Conduct regular staff training on HIPAA regulations. #### PCI-DSS - **Encryption:** Protect cardholder data through strong encryption methods. - **Access Control:** Implement strict access controls for systems storing cardholder data. - **Monitoring and Testing:** Regularly test security systems and processes. #### SOX - **Audit Trails:** Maintain comprehensive audit logs of all transactions and access to data. - **Data Integrity Controls:** Ensure controls are in place to maintain data integrity. - **Regular Audits:** Conduct regular compliance audits. #### COPPA - **Parental Consent:** Implement mechanisms to obtain verifiable parental consent. - **Data Deletion Policy:** Ensure the ability to delete children’s data upon request. #### Data Residency Laws - **Data Localization:** Store data within specified geographic boundaries as required. - **Compliance Monitoring:** Regularly review compliance with local data residency laws. ### 8.3. Data Subject Rights | Right | Description | |-----------------------------|--------------| | Right to Access | Users can request access to their personal data. | | Right to Rectification | Users can request corrections to inaccurate personal data. | | Right to Erasure | Users can request deletion of their personal data. | | Right to Object | Users can object to the processing of their data under certain conditions. | | Right to Data Portability | Users can request their data in a structured format for transfer. | ### 8.4. Privacy Requirements **Consent:** Users must provide explicit consent for data collection and processing activities. **Privacy Notice:** A clear privacy policy must be provided, detailing data collection, usage, sharing, and user rights. **Data Usage Policies:** Define how data will be used, including analytics and third-party sharing. ### 8.5. Audit and Monitoring Requirements **Logging:** Maintain detailed logs of access to personal data and security events for accountability. **Monitoring:** Implement continuous monitoring of data access and processing activities for compliance. **Incident Response:** Establish an incident response plan to address data breaches and security incidents. ### 8.6. Data Handling Rules **Retention:** Data should only be retained as long as necessary for its intended purpose, with defined retention periods. **Deletion:** Implement secure deletion protocols for personal data no longer required. **Data Security:** All sensitive data must be secured through encryption and access controls. ### 8.7. Compliance Risk Assessment The compliance risk assessment identifies potential risks associated with regulatory compliance and security controls that must be addressed to mitigate these risks. **Key Compliance Risks:** - Mismanagement of personal data leading to GDPR and CCPA violations. - Inadequate protection of health-related information, risking HIPAA non-compliance. - Failure to protect payment information, leading to PCI-DSS violations. - Insufficient audit trails and documentation, risking SOX compliance breaches. --- ## 9. Security Architecture Recommendations This section provides comprehensive security architecture guidance that integrates security controls into the system's technical design. Security architecture defines how security principles, controls, and patterns are applied across system components to create a cohesive, defense-in-depth security posture. The recommendations address architectural principles, component-level controls, data protection strategies, and third-party integration security to ensure security is built into the system design. ### 9.1. Architectural Security Principles Architectural security principles provide the foundational philosophy guiding all security design decisions. These principles ensure a consistent security posture across all system components and guide the selection and implementation of security controls, balancing protection, usability, and performance for an AI-powered code review and security analysis platform. - **Zero Trust Architecture principles**: Never trust, always verify — all requests (users, services, devices) are authenticated and authorized regardless of network location or origin. This reduces implicit trust assumptions and limits lateral movement if any component is compromised. - **Defense in Depth**: Multiple, independent layers of controls (network, application, data, identity, monitoring) reduce the chance a single failure leads to a breach; each layer compensates for weaknesses in others. - **Principle of Least Privilege**: Grant users and services only the minimum privileges required to perform tasks, both for repository access and model operations; reduces blast radius of credential compromise. - **Secure by Default / Secure by Design**: Services should ship with secure defaults (e.g., strong crypto, logging, RBAC) and security decisions considered at design time to avoid retrofitting. - **Separation of Duties**: Split critical functions across roles and systems (e.g., model training vs. inference, audit management vs. remediation approvals) to prevent conflicts of interest and reduce risk of insider abuse. - **Fail Secure (Fail Closed)**: In failure scenarios, systems should deny sensitive operations rather than fail open; e.g., if an auth check cannot be completed, deny access. - **Complete Mediation**: Every access request must be checked against current policy (no caching of authorization decisions beyond their valid TTL) to ensure policy changes take immediate effect. - **Defense-in-Depth for Data**: Combine encryption, access controls, redaction and data minimization for sensitive artifacts (code snippets, tokens, PII) so data compromise is hard to exploit. - **Model Governance & Responsible ML**: Govern model lifecycle with versioning, testing, provenance, drift detection, and human-in-the-loop safeguards for high-impact suggestions. - **Privacy by Design**: Treat customer code and any PII in it as sensitive; embed consent capture, opt-in/opt-out training, anonymization and minimization into data pipelines. - **Auditability & Tamper-Evident Logging**: Capture rich, correlated telemetry for all actions (scans, PRs, model inferences) and protect logs with append-only storage and cryptographic integrity to support forensics and compliance. - **Resilience and Availability**: Design for graceful degradation and abuse protection (rate limiting, quotas, DDoS mitigation) to maintain availability under load or attack. --- ### 9.2. Component-Level Security Controls #### Edge/API Gateway **Required Controls:** - OAuth2/OIDC integration for user and repository authorization - Mutual TLS (mTLS) for backend service communication where supported - JWT validation and token introspection with short lifetimes and refresh policies - Request rate limiting and per-actor quotas - Web Application Firewall (WAF) and DDoS protection integration - Input validation and payload size limits for uploads (e.g., code diffs) - IP allow/deny lists for management/administrative endpoints - Centralized request logging with correlation IDs and structured telemetry - TLS 1.3 enforcement and strict cipher suites - RBAC enforcement at the gateway for coarse-grained routing decisions **Recommended Patterns:** - API Gateway with OAuth2, OIDC and scoped tokens - Edge WAF and API layer DDoS protection (e.g., managed WAF + CDN) - Short-lived JWT + refresh tokens with centralized session revocation endpoint - Edge-based rate limiting and per-tenant quotas - Canary routing for new model versions and A/B testing via gateway rules #### Frontend User Interface **Required Controls:** - Enforce authentication via SSO (SAML/OIDC) with MFA for admin roles - Per-user RBAC and permission checks on every API call - Content Security Policy (CSP), X-Frame-Options, and secure cookies (HttpOnly, Secure, SameSite) - Input validation and output encoding to prevent XSS - Session management with short idle/absolute timeouts and revocation support - Client-side redaction/obfuscation of secrets when displaying suggestions - Client telemetry with user consent and privacy-preserving defaults **Recommended Patterns:** - Single Page App (SPA) served via CDN with strict CSP and Subresource Integrity - Use OAuth/OIDC implicit or auth-code + PKCE flow delegated to gateway - Role-based UI rendering with server-side authorization checks - Frontend feature flags for progressive exposure of auto-fix capabilities #### Application Services **Required Controls:** - Strong service-to-service authentication (mTLS or short-lived service tokens) - Fine-grained RBAC for repository operations, PR creation, approvals - Input validation and sanitization for all external inputs (webhooks, code uploads) - Audit logging for all actions (scans triggered, PRs created, approvals, model training requests) - Secrets vault integration for storing integration tokens and keys - Rate limiting and back-pressure handling for scan orchestration - Secure CI/CD integration using ephemeral credentials and least-privilege runners **Recommended Patterns:** - Microservices with API Gateway fronting and internal service mesh (mTLS, network policies) - Workflow orchestration via message queues with signed job tokens and idempotency keys - Use of Dedicated Service Accounts per integration with least privilege - Outbound webhook signature verification for incoming repo events #### AI Analysis Engine **Required Controls:** - Isolated execution contexts for analysis jobs with process/file system separation - Input sanitization and secret detection prior to passing code into models - Redaction of sensitive data from model prompts/responses and stored artifacts - Resource quotas per tenant plus rate limiting on inference calls - Logging of model inputs/outputs with privacy-preserving sanitization and provenance metadata - Hardened containers/VMs for model runtime with runtime integrity checks - Dependency and SAST tool sandboxing; scanned tools run with minimal privileges **Recommended Patterns:** - Dedicated inference cluster with namespace isolation per tenant (Kubernetes namespaces, VPCs) - Use of container-native security (gVisor, Kata Containers) for additional isolation - Pre-processing pipeline: secret-scan -> redaction -> tokenize -> inference -> post-process - Hybrid inference: managed-host for public SaaS, on-prem model hosting for private deployments #### Model Management **Required Controls:** - Model artifact repository with integrity checks (hashing, signatures) - Model versioning, promotion gates, and rollback capability - Access controls for model training, deployment, and download - Audit trail for training data provenance and model changes - Performance monitoring, drift detection and alerting - Isolation for customer-specific model training (tenant VPCs or on-prem training nodes) - Controls to prevent leakage of training data in inference (privacy-preserving techniques) **Recommended Patterns:** - Model registry with signed artifacts and immutable metadata (e.g., MLflow + artifact signing) - CI/CD for models with automated tests, security checks and manual approval for production models - A/B testing routing layer with canary percentages and automatic rollback on regressions - Differential privacy / federated learning options for sensitive training datasets #### Data Storage & Persistence **Required Controls:** - Field-level encryption for sensitive fields (tokens, secrets, PII) - KMS-managed envelope encryption and BYOK options for enterprise customers - Immutable audit log storage (WORM) for findings and remediation history - Access controls and DB encryption at rest (TDE) and network isolation - Data classification tagging and per-tenant data partitions - Backup encryption and secure backup lifecycle management - Secure deletion workflows and verified erasure for deletion requests **Recommended Patterns:** - Encrypted object storage for code artifacts with KMS envelope encryption - Database encryption (TDE + column-level encryption) plus separate DB roles for access - Use of immutable object storage for audit logs with append-only retention policies - Tenant-scoped storage buckets or DB schemas for data separation #### External Integrations **Required Controls:** - Secure credential storage for integration tokens (vault) - Verification of webhook payload signatures, replay protection and throttling - Least-privilege OAuth scopes and periodic token rotation - Monitoring, logging and alerting for integration failures and anomalous activity - Isolation of connectors and outbound network egress controls **Recommended Patterns:** - Connector microservices running in restricted network zones with egress controls - Use of ephemeral credentials and OAuth tokens bound to connector instances - Circuit breakers for unreliable third-party endpoints and retry/backoff policies - Central policy engine to control what integrations may access and which events can be forwarded --- ### 9.3. Data Protection Strategy **Data Classification:** Public, Internal, Confidential, Restricted - Public: Non-sensitive documentation, marketing materials, generic scan metrics without tenant-identifying info. - Internal: Operational telemetry, non-sensitive aggregated metrics and dashboards. - Confidential: Repository metadata, scan job metadata, aggregated scan results that are tenant-identifiable, model telemetry. - Restricted: Source code snippets/diffs, secrets (API keys, tokens), PII contained in code/config, customer training datasets, KMS keys and BYOK materials, audit evidence used in compliance packages. **Encryption Requirements:** - Data in transit: TLS 1.3 with strong ciphers (AEAD suites); enforce TLS 1.3+; perfect forward secrecy (ECDHE). For internal service-to-service communication prefer mTLS (TLS 1.3). - Data at rest: AES-256-GCM for object storage and DB field-level encryption; database Transparent Data Encryption (TDE) with AES-256. - Key management: KMS-based envelope encryption; use RSA-4096 or ECC P-384/ECDSA for key signing where applicable. Support BYOK (customer-managed keys) through secure import or dedicated KMS federation. - Log integrity: HMAC-SHA256 or SHA-512 signatures for log blocks, with periodic notarization (e.g., Merkle tree or blockchain-backed timestamping for high-assurance tenants). **Retention Policies:** - Default retention: Keep analysis artifacts and scan metadata for 90 days for operational use; aggregated metrics indefinitely (anonymized). - Compliance retention: Allow configurable retention per tenant to meet regulatory obligations (e.g., GDPR minimum necessary period, ability to specify longer retention for audit). - Audit/evidence: Retain signed audit/evidence packages for at least the period required by the customer’s compliance (default 7 years for regulated customers if requested). - Secrets/exposed artifacts: If secrets are detected and recorded in the vault for remediation workflows, mark them restricted and retain only for 30 days unless required longer by incident response — provide automated purge workflows. - Backups: Encrypted backups retained according to policy (default 90 days), with longer cold archives as required by customers (WORM support/append-only for compliance). **Handling Procedures:** - Ingress: Code snippets are ingested via authenticated webhooks/CI connectors; pre-process immediately with secret detection and classification. Any detected secret triggers immediate customer notification and optional blocking. - Access: Enforce RBAC and least privilege for read/write access to stored artifacts; require just-in-time elevation for sensitive operations and log all access. - Transmission: Always over TLS 1.3; for internal control planes use mTLS and internal network segmentation (private subnets/VPCs). - Storage: Apply tenant-scoped segmentation and encryption at rest; redaction of secrets and PII before persisting to long-term storage unless customer explicitly opts into storing raw artifacts for diagnostics (explicit consent required). - Training Data: Respect opt-in/opt-out tags; sanitize and redact secrets/PII before inclusion; use differential privacy or federated learning for aggregate improvements. - Deletion: Implement cryptographically verifiable deletion for object stores and databases; honor customer deletion requests and propagate deletion to backups within a defined SLA (e.g., 30-90 days depending on backup schedule). - Export: Exports of compliance evidence must be signed and transmitted over secure channels; require authorization and log export events. - Incident Response: For leaked secrets or unauthorized access, immediate revocation of affected tokens/keys (via KMS and connectors), rotation workflows, and automatic creation of incident tickets. --- ### 9.4. Third-Party Integration Security **GitHub / GitLab / Bitbucket** *Security Requirements:* - OAuth app with minimum scopes and explicit consent per repository - Webhook payload signature verification (HMAC) and replay protection - Storage of integration tokens in vault with rotation - Granular per-repo permission mapping and token-scoped access control *Risk Assessment:* High - direct access to source code and ability to create PRs; compromise could expose proprietary code or allow unauthorized code changes. *Recommended Controls:* - Per-repo least-privilege OAuth scopes and periodic scope review - Validate webhook signatures and reject unsigned requests - Rate limit webhook processing and apply payload size limits - Store tokens in vault (KMS-backed) and rotate automatically; use short-lived installation tokens where possible - Map repo identities to internal RBAC and require admin approval for enabling integrations **CVE/NVD Feeds** *Security Requirements:* - Secure ingestion over HTTPS with feed integrity verification (signed feeds where available) - Validation and normalization of feed data before mapping to SBOM/component inventory - Timestamp validation and replay protection for feed updates *Risk Assessment:* Medium - feed tampering or poisoning could produce false positives/negatives in vulnerability mapping. *Recommended Controls:* - Validate feed checksums/signatures and use mirror checks from reputable sources - Maintain an independent verification and staging pipeline for new feed entries - Alert on anomalous feed patterns and enable manual vetting for critical CVEs **Slack** *Security Requirements:* - Use app-level tokens with least privileged scopes - Verify webhook/event signatures and use dedicated channels for notifications - Redact sensitive content before sending notifications *Risk Assessment:* Medium - notifications may leak sensitive info if not redacted; token compromise could be used for social engineering. *Recommended Controls:* - Store Slack tokens in vault and rotate periodically - Redact secrets and sensitive code fragments from messages - Restrict channels used for critical alerts and require approval to forward full findings externally **Microsoft Teams** *Security Requirements:* - Use dedicated app registrations with OAuth and least privileged scopes - Validate outgoing connector authenticity - Redaction of sensitive data in notifications *Risk Assessment:* Medium - similar to Slack, risk of leakage and token misuse. *Recommended Controls:* - Token vaulting and rotation - Teams channel access control and scoped connectors - Rate limiting and content filters on notifications **CI/CD Systems (GitHub Actions, Jenkins, GitLab CI)** *Security Requirements:* - Use ephemeral, least-privilege runner credentials and short-lived tokens - Validate job origin and image provenance for runner execution - Integration only via secure endpoints and scoped tokens *Risk Assessment:* High - CI/CD agents can execute arbitrary code; compromised CI could weaponize auto-PRs or auto-fixes. *Recommended Controls:* - Use ephemeral credentials bound to specific jobs and restrict runner networks - Run SAST/dependency scanning in isolated sandboxed runners - Enforce signed commits / require verified signatures for auto-applied changes - Integrate PR gating and require human approval for high-risk auto-fixes **SSO Providers (SAML/OIDC Identity Providers)** *Security Requirements:* - Enforce MFA for privileged roles - Implement strict redirect URI validation and token lifetime policies - Audit and log SSO assertions and authentication flows *Risk Assessment:* High - SSO compromise could yield broad platform access. *Recommended Controls:* - Enforce SSO with conditional access policies and MFA - Periodic access reviews and automated deprovisioning workflows - Use SCIM for automated provisioning and disable stale accounts **Managed Model Hosts / GPU Providers** *Security Requirements:* - Network isolation for model hosts and secure management plane access - Customer data handling agreements for datasets used in custom training - Ensure hypervisor/container isolation and secure image provenance *Risk Assessment:* High for multi-tenant hosted models — risk of data leakage, model theft, or cross-tenant inference leakage. *Recommended Controls:* - Tenant isolation via VPCs, dedicated instances or hardware enclaves for sensitive training - Encrypted disk and in-memory protections (where available) - Strict access control to training logs and artifacts; sign models before export **SIEM / Monitoring Providers** *Security Requirements:* - Secure log forwarding (TLS) with authentication and integrity checks - Role-based access to SIEM dashboards and retention configurations - Ensure log redaction for sensitive fields before forward *Risk Assessment:* Medium - logs contain sensitive metadata that could be abused if leaked. *Recommended Controls:* - Use secure collectors with vetting and filtering rules - Implement selective forwarding and redaction of PII/secrets - Validate retention and access controls in SIEM **Backup / Archival Providers** *Security Requirements:* - Encrypted backups with KMS control and WORM/append-only options for audit artifacts - Access controls and audit logging for backup retrieval and restores *Risk Assessment:* Medium - backups hold full datasets; compromise could lead to exfiltration of historical code/artifacts. *Recommended Controls:* - Enforce BYOK for enterprise customers and access audit trails - Use immutable backup storage and monitor backup access - Periodic restoration tests for integrity and secure deletion tests --- (End of document) --- ## 10. Implementation Roadmap This section provides a prioritized, phased approach for implementing the security controls identified throughout this analysis. The roadmap organizes security measures into logical phases based on risk, dependencies, and resource availability, ensuring critical security gaps are addressed first while building a foundation for comprehensive security coverage. ### 10.1. Prioritization Framework Provide a comprehensive explanation of the prioritization approach used to organize security controls into implementation phases. Begin with a paragraph explaining why prioritization is critical for effective security implementation, then explain each criterion: Prioritization of security controls is critical to ensure that resources are allocated efficiently, addressing the most pressing security threats and compliance requirements first. It is essential to balance immediate risk mitigation with long-term strategic goals, taking into account technical and resource constraints. This structured approach ensures that critical vulnerabilities are addressed promptly while setting the stage for ongoing security enhancements. **Prioritization Criteria:** - **Risk Level**: Controls addressing critical and high-risk threats (identified through threat modeling) are prioritized first - **Compliance Deadlines**: Regulatory requirements and compliance deadlines influence immediate priority - **Technical Complexity**: Controls requiring foundational infrastructure are implemented early to enable subsequent controls - **Dependencies**: Controls that other security measures depend upon are prioritized accordingly - **Resource Availability**: Implementation considers the availability of skilled personnel, tools, and budget - **Business Impact**: Controls protecting business-critical functions and data receive higher priority These criteria work together to create a logical implementation sequence that balances security needs with practical constraints, ensuring that the most critical issues are addressed first while laying the groundwork for future improvements. ### 10.2. Phased Implementation Plan #### Phase: IMMEDIATE **Timeline:** 0-1 months **Rationale:** This phase focuses on addressing critical vulnerabilities and compliance blockers that pose immediate risks to the system's security and operational integrity. **Controls to Implement:** - Harden authentication and token management, including short-lived tokens and JWT validation - Implement basic encryption for sensitive data both at rest and in transit - Address critical vulnerabilities in the system as identified in threat modeling - Establish essential access controls and secure OAuth integrations **Dependencies:** - Availability of skilled security engineers to implement authentication and encryption mechanisms #### Phase: SHORT-TERM **Timeline:** 1-3 months **Rationale:** These controls build upon immediate security measures, focusing on improving access control adjustments and ensuring that logging and API security mitigate identified threats effectively. **Controls to Implement:** - Enhance user authentication through comprehensive multi-factor authentication - Deploy role-based access controls across the admin dashboard - Implement comprehensive logging and monitoring for all administrative actions - Strengthen API security with input validation and HTTPS protocols - Begin encryption for all sensitive data at rest **Dependencies:** - Completion of TLS Implementation - Completion of multi-factor authentication #### Phase: MEDIUM-TERM **Timeline:** 3-6 months **Rationale:** This phase introduces more advanced security controls that require additional planning and resource allocation, focusing on threat detection and third-party security audits. **Controls to Implement:** - Deploy advanced threat detection systems to identify and mitigate potential security incidents - Automate security testing within development pipelines to ensure continuous security validation - Conduct third-party security audits to validate security posture and compliance - Enhance data protection mechanisms, including data masking and tokenization **Dependencies:** - Completion of logging and monitoring systems - Availability of third-party audit services #### Phase: LONG-TERM **Timeline:** 6-12 months **Rationale:** These strategic initiatives aim to enhance security maturity and integrate advanced AI/ML security controls, ensuring a robust security posture over the long term. **Controls to Implement:** - Develop and implement security maturity enhancements across the organization - Integrate advanced AI/ML security controls into the platform for proactive threat detection - Conduct comprehensive penetration testing to identify and mitigate potential vulnerabilities - Launch security awareness programs to educate staff on best practices and emerging threats **Dependencies:** - Completion of advanced threat detection systems - Availability of resources for security awareness training #### Phase: ONGOING **Timeline:** Continuous **Rationale:** Continuous activities ensure ongoing security maintenance and readiness to respond to incidents as they arise. **Controls to Implement:** - Maintain continuous security monitoring and alerting through integrated SIEM solutions - Implement regular patch management processes to keep systems updated against vulnerabilities - Conduct periodic compliance audits to ensure adherence to regulatory requirements - Maintain incident response readiness with regular drills and updates to response plans **Dependencies:** - Availability of skilled personnel for continuous monitoring and incident response - Integrated tools for SIEM and patch management ### 10.3. Resource Requirements Skills: Security engineers, Security architects, Web developers, Compliance specialists Recommended tools: SIEM solutions for logging and monitoring, Vulnerability scanners for testing, Encryption libraries for data protection, API management tools for secure interfaces Estimated time effort: Approximately 3-6 months for initial phases, with ongoing efforts extending resources as per system complexity and requirements. --- ## 11. Verification and Testing Strategy ### 11.1. Testing Approach Integrate security testing throughout the software development lifecycle (SDLC) with an emphasis on continuous security practices. Balance automated scanning with manual evaluations to prioritize high-risk areas based on business impact, adhering to shift-left security principles by incorporating security testing earlier and continuously. This approach ensures that vulnerabilities are identified and remediated as early as possible, minimizing risks and enhancing overall code security. ### 11.2. Testing Methods | Method | Frequency | Tools | |-------------------------------------------------|----------------------------|-------------------------------------------------------------| | STATIC APPLICATION SECURITY TESTING (SAST) | Every commit/build | SonarQube, Semgrep, Checkmarx, CodeQL | | DYNAMIC APPLICATION SECURITY TESTING (DAST) | Nightly/weekly | OWASP ZAP, Burp Suite, Acunetix | | DEPENDENCY SCANNING | Every build | Snyk, Dependabot, OWASP Dependency-Check | | SECRETS SCANNING | Every commit | TruffleHog, GitLeaks, GitHub Secret Scanning | | CONTAINER/INFRASTRUCTURE SCANNING | Every deployment | Trivy, Clair, Prowler, ScoutSuite | | PENETRATION TESTING | Quarterly or before major releases | Custom scripts, Metasploit, Burp Suite Pro | | SECURITY CODE REVIEW | For critical features | GitHub/GitLab code review, security checklists | | COMPLIANCE SCANNING | Continuous | AWS Config, Azure Policy, Cloud Custodian | ### 11.3. Compliance Verification Multi-standard compliance (OWASP ASVS, NIST SP 800-53, ISO 27001) will be verified through automated tools and manual checks against regulatory requirements such as GDPR, CCPA, and PCI-DSS. Audit preparation will involve ensuring documentation and evidence collection for external audits. Recommendations will include engaging third-party auditors for comprehensive evaluations, ensuring continuous alignment with regulatory standards and best practices for data protection and security. ### 11.4. Continuous Monitoring Implement Security Information and Event Management (SIEM) for real-time monitoring, supported by Intrusion Detection/Prevention Systems (IDS/IPS) to identify and mitigate threats. All logs will be aggregated and analyzed for anomalies, facilitating rapid incident detection and response. Continuous monitoring will include behavior analytics to identify potential misuse or security breaches, with integration into incident response processes to ensure prompt action against security events. ### 11.5. Key Performance Indicators (KPIs) - Mean time to detect (MTTD) security issues - Mean time to remediate (MTTR) vulnerabilities - Percentage of critical vulnerabilities patched within SLA - Security test coverage percentage - False positive rate in automated scanning - Compliance audit pass rate --- ## 12. Validation Report This section presents a comprehensive validation of the security requirements generated throughout this analysis. The validation evaluates the requirements against five key dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. This assessment ensures that the security requirements are comprehensive, technically sound, and actionable for implementation teams. ### 12.1. Overall Assessment The overall validation score reflects the quality and completeness of the security requirements across five critical dimensions. Each dimension is scored from 0.0 to 1.0, with 1.0 representing excellent coverage and 0.0 indicating significant gaps. **Overall Score:** 0.92/1.0 **Validation Status:** ✅ PASSED The security requirements have met the quality threshold (≥0.8) and are ready for implementation. The requirements demonstrate comprehensive coverage, technical accuracy, and alignment with business objectives. The validation assesses: - **Completeness**: Are all identified security concerns adequately addressed? - **Consistency**: Do requirements align with each other without contradictions? - **Correctness**: Are controls appropriate for the identified risks and correctly applied? - **Implementability**: Are requirements specific, actionable, and feasible to implement? - **Alignment**: Do security requirements align with business requirements and objectives? ### 12.2. Dimension Scores | Dimension | Score | Status | |-----------|-------|--------| | Completeness | 0.92 | ✅ | | Consistency | 0.98 | ✅ | | Correctness | 0.90 | ✅ | | Implementability | 0.86 | ✅ | | Alignment | 0.95 | ✅ | **Score Interpretation:** - ✅ 0.8-1.0: Excellent - ⚠️ 0.7-0.79: Acceptable (minor improvements needed) - ❌ <0.7: Needs significant improvement ### 12.3. Detailed Feedback The provided security requirements and controls are thorough and map well to the original business requirements. Strengths: repository integration/OAuth, RBAC, encryption/KMS, logging/auditability, dependency and secret scanning, CI/CD integration, model lifecycle controls, compliance mappings (GDPR/CCPA/HIPAA/PCI/etc.), and AI/ML high-level threats and monitoring are all addressed with relevant standards and verification guidance. Actionable improvements (prioritized): 1) Make AI-specific mitigations more concrete and testable - Define specific prompt-injection mitigations: use deterministic prompt templates, explicit context length limits, prompt sanitizers, allow/deny token lists, and input provenance tagging. Add unit/integration tests that inject malicious payloads to verify sanitizer and template behavior. - Output redaction/filtering: specify which PII/secret patterns must be redacted, implement field-level redaction rules and a verification test suite that seeds PII and secrets to confirm removal before UI/API exposure. - Differential privacy & DP parameters: if training on customer code, specify DP algorithms and epsilon bounds or other privacy guarantees; add acceptance tests that measure privacy leakage risk. - Model poisoning/malicious training data controls: require signed/traced training data, dataset provenance metadata, ingestion whitelists, anomaly detection on training-set statistics, and held-out test failure thresholds to gate model promotion. 2) Expand observability and automated detection for AI components - Define concrete metrics & alert thresholds (e.g., sudden change in suggestion rejection rate > X% in 24h, spike in identical outputs, drift in F1/precision by Y). Integrate these into SIEM runbooks with response actions and automated rollback gates. - Add detection tests for adversarial inputs and continuous adversarial evaluation in CI for model updates. 3) Harden model inference runtime and data flow - Specify runtime isolation: container/microVM per tenant or strict multi-tenancy isolation; hardware enclave or HSM use for model keys if required by data residency. - Define network egress policies for inference VPCs and enforce allowlists to prevent exfiltration. - Mandatory request/response size limits, rate limits per tenant and per API key with precise quotas (e.g., 60 requests/min/user) and documented throttle behavior. 4) Make requirements more implementable by adding quantitative acceptance criteria - For encryption: state accepted algorithms and key rotation cadence (e.g., AES-256 at rest, TLS1.2+; KMS keys rotated every 90 days). - For logging/audit: retention periods per regulation (e.g., 1 year for general logs, 7 years for SOX evidence) and guarantee of append-only or cryptographic signing for log integrity. - For caching: define TTLs per artifact class and invalidation triggers (e.g., cache invalidate on commit/PR within 30s) and stress test objectives. - For access control: define role matrix and SSO protocols supported (OAuth2, SAML 2.0, OIDC), and deprovisioning target SLA (e.g., within 15 minutes of account removal). 5) Fill missing or underspecified areas - Secrets handling: require integration with a vault (e.g., HashiCorp Vault or cloud KMS secret manager), ephemeral credential issuance patterns, and mandatory secret rotation workflows for exposed secrets. - Model supply chain: require cryptographic signatures for externally sourced models, SBOM-like metadata for models, and integrity verification checks during model import. - Disaster recovery / business continuity: add RPO/RTO targets for critical services (inference API, scanning pipelines, dashboard) and periodic DR testing. - Data retention and deletion: enumerate retention periods and secure deletion mechanisms per data class; include proof of deletion for customer opt-out requests. 6) Improve testability and traceability - Provide a traceability matrix mapping each high-level requirement to specific controls, ownership, and test cases (unit/integration/pen-test requirements). - Define a baseline threat model with prioritized attack trees per component and measurable mitigation status (e.g., PROMPT-INJ: mitigated/partially mitigated/unmitigated). 7) Compliance and consent operationalization - Operationalize consent: define data model for consent metadata, APIs to capture/revoke consent, and enforcement in the training pipeline that rejects data without opt-in. Provide audit logs proving enforcement. - Map data residency controls to deployment automation: require tagging, enforcement policies, and automated verification tests that assert no cross-border data transfer. Suggested immediate next steps - Add explicit acceptance criteria for each high-level requirement (security user stories with tests and SLOs). - Extend AI/ML controls to include concrete metrics, DP parameterization, signed dataset provenance, runtime isolation, and adversarial testing procedures. - Produce a requirements-to-test matrix and update the control mappings with precise numeric thresholds, tools, and owners. With these targeted clarifications and quantitative gates, the requirements will be fully implementable, testable, and resilient to AI-specific and platform threats. --- ## Appendix A: Original Requirements Document ``` AI-Powered Code Review and Security Analysis Platform Requirements We need to build an AI-powered platform that automatically reviews code commits, identifies security vulnerabilities, and provides intelligent remediation suggestions for development teams. Key Features: 1. Repository Integration Connect to GitHub, GitLab, and Bitbucket repositories OAuth-based authentication for repository access Webhook integration for real-time commit monitoring Support for multiple programming languages (Python, Java, JavaScript, Go, etc.) 2. AI-Powered Analysis Engine LLM-based code review using fine-tuned models (GPT-4, CodeLlama, or custom models) Static code analysis for vulnerability detection Pattern recognition for common security anti-patterns Dependency scanning for known CVEs Secret detection in code and configuration files 3. Intelligent Remediation AI-generated fix suggestions with code snippets Automatic pull request creation with security fixes Severity scoring and risk assessment Context-aware explanations of vulnerabilities 4. Team Collaboration Developer dashboards with personalized security metrics Comment threads on identified issues Integration with existing CI/CD pipelines Slack/Teams notifications for critical findings 5. Compliance and Reporting OWASP Top 10 compliance checking Custom security policy enforcement Audit logs of all security findings Export reports for compliance documentation Trend analysis of security posture over time 6. Model Management API endpoints for model inference Model versioning and A/B testing Custom model training on organization's codebase Model performance monitoring and drift detection 7. Data Handling Process and store code snippets for analysis Maintain vulnerability database Store remediation history and patterns Cache analysis results for performance The platform will handle sensitive source code, authentication tokens, API keys, and potentially expose AI models through APIs. It requires integration with various third-party services, processes potentially proprietary code, and needs to maintain strict access controls while providing actionable security insights. ``` --- ## Appendix B: Glossary | Term | Definition | |------|------------| | ASVS | Application Security Verification Standard (OWASP) | | STRIDE | Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege | | SAST | Static Application Security Testing | | DAST | Dynamic Application Security Testing | | MFA | Multi-Factor Authentication | | RBAC | Role-Based Access Control | | PII | Personally Identifiable Information | | PHI | Protected Health Information | | GDPR | General Data Protection Regulation | | HIPAA | Health Insurance Portability and Accountability Act | | PCI-DSS | Payment Card Industry Data Security Standard | --- ## Appendix C: Complete Threat List This appendix contains the complete list of all identified threats with full descriptions and mitigation strategies. Threats are organized by risk level for easy reference. ### Critical Risk Threats **THR-001** - Edge/API Gateway - **Category:** Spoofing - **Likelihood:** High | **Impact:** High - **Risk Level:** Critical - **Description:** Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsigned/weakly-signed tokens) allowing an attacker to impersonate users or service accounts and access repositories, create scans, or view results. - **Mitigation Strategy:** Enforce strict OAuth flows and short-lived tokens, validate JWT signatures and claims (iss/aud/exp), use proof-of-possession or mTLS for high-priv tokens, rotate tokens frequently, require token binding for webhooks, monitor for suspicious token usage, rate-limit token usage, and implement device/session fingerprinting. Enforce scope minimization on OAuth clients. **THR-009** - AI Analysis Engine - **Category:** Information Disclosure - **Likelihood:** High | **Impact:** High - **Risk Level:** Critical - **Description:** Sensitive source code or secrets are transmitted to or stored on external managed model hosts (SaaS model providers) where tenant separation, contractual protections, or inadvertent logging could lead to leakage of proprietary code or credentials. - **Mitigation Strategy:** Offer private/on-prem model deployment options, require E2E encryption (client to model), ensure model hosts provide data isolation contracts, avoid logging raw code snippets at the model host, minimize data sent to external providers (use extracted features), use BYOK and VPC peering for private deployments, and provide explicit opt-in for sending full code to managed hosts. ### High Risk Threats **THR-002** - Edge/API Gateway - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Manipulation of inbound API requests or webhook payloads en route to backend (e.g., altering scan parameters, redirecting callbacks, or injecting malicious fields) to change scan behavior or escalate access. - **Mitigation Strategy:** Validate webhook signatures (HMAC), verify sender IPs where feasible, use mutual authentication (mTLS) for integrations, perform strict input validation and schema checks at gateway, sign and/or timestamp important parameters, and record an immutable request audit trail. **THR-003** - Edge/API Gateway - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** API gateway or edge infrastructure overwhelmed by high request volume or crafted requests (flooding, slowloris, large payloads) causing availability loss for scanning and developer access. - **Mitigation Strategy:** Implement rate limiting, request throttling, bot detection, WAF and DDoS protection, circuit breakers, autoscaling with quotas, request size limits, and progressive backoff for heavy analysis requests. Use API keys and quotas per tenant. **THR-004** - Frontend Layer - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, findings or tokens to other users or remote hosts; stored XSS in comment threads revealing sensitive diffs. - **Mitigation Strategy:** Apply strict output encoding/escaping, implement a strong Content Security Policy (CSP), sanitize user inputs, adopt secure templating frameworks, use HttpOnly and Secure cookies, enforce same-site cookie attributes, and perform regular frontend security testing. **THR-006** - Application Services - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Forged repository webhook events or API calls cause the application to perform unauthorized actions such as merging PRs, applying fixes, or changing RBAC settings. - **Mitigation Strategy:** Verify webhook signatures, use per-integration secrets, enforce least privilege for integration accounts, require branch protection and signed commits for automatic merges, and add manual approvals for high-impact automated fixes. **THR-008** - Application Services - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Broken access control leading to unauthorized users (or tenants) reading repository metadata, scan results, vulnerability details, or remediation suggestions. - **Mitigation Strategy:** Enforce RBAC/ABAC with least privilege, implement strong multi-tenant isolation, perform authorization checks server-side for each resource, adopt just-in-time access, and provide fine-grained data redaction/scoping. **THR-010** - AI Analysis Engine - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Poisoning of training data or prompt injection via crafted code causing models to generate insecure or malicious remediation suggestions (e.g., suggesting credential exfiltration or insecure configurations). - **Mitigation Strategy:** Validate and sanitize training data, maintain provenance of training artifacts, isolate customer training sets, perform adversarial testing and red-team model evaluation, implement prompt and output filtering, and require deterministic SAST cross-checks for suggested fixes. **THR-011** - AI Analysis Engine - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** LLM-generated remediation automatically introduces code that elevates privileges (e.g., creating backdoor admin endpoints or insecurely setting permissions) and is merged without adequate review. - **Mitigation Strategy:** Require human review or automated safety checks for high-risk code changes, implement automated SAST/IAST validation of suggested patches, prevent automatic merges for critical severity changes, and enforce branch protections and signed approvals for elevated-permission changes. **THR-012** - AI Analysis Engine - **Category:** Denial of Service - **Likelihood:** High | **Impact:** Medium - **Risk Level:** High - **Description:** Excessive inference requests (automated or malicious) saturate model hosting or GPU capacity, causing analysis delays or denial of analysis service to other tenants. - **Mitigation Strategy:** Implement per-tenant quotas, rate-limiting, queueing/backpressure, autoscaling of inference clusters, graceful degradation to lightweight scanners, and cost-control safeguards. Monitor usage and detect anomalous patterns. **THR-014** - Secret Detection / Data Storage - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Secrets (API keys, tokens, private keys) stored in persistent storage, caches, backups, logs, or remediation history in plaintext or weakly encrypted form leading to leak and unauthorized access to third-party systems. - **Mitigation Strategy:** Encrypt secrets at rest using KMS/BYOK, use envelope encryption, redact secrets from UI/logs, avoid storing full secrets (store hashed/hashed pointers), rotate secrets on detection, implement strict access controls and auditing for key material. **THR-015** - Model Management - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Unauthorized modification or replacement of model binaries or metadata (model poisoning or substitution) causing compromised inference results across tenants. - **Mitigation Strategy:** Secure model registries with RBAC, sign model artifacts, use integrity checks (hashes/signatures), store models in hardened immutable storage, require approvals for model changes, and log/model change audit trails. **THR-016** - Model Management - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Leakage of model binaries or customer-specific training data/artifacts used for fine-tuning (e.g., code used in training accessible to other customers or leaked externally). - **Mitigation Strategy:** Isolate customer training environments (VPC/on-prem), encrypt training datasets and artifacts, use access controls and logging for training storage, avoid multi-tenant mixing of data during training, and provide contractual controls for managed hosts. **THR-017** - Data Storage & Persistence - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Public exposure of backups, snapshots, or misconfigured storage buckets containing code artifacts, vulnerability DB, or audit logs (e.g., publicly accessible S3 bucket). - **Mitigation Strategy:** Enforce least-privilege IAM for storage, block public access by default, encrypt backups with KMS/BYOK, perform automated scans for open storage, enforce MFA and approval for snapshot exports, and implement immutable retention where required. **THR-018** - Data Storage & Persistence - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Unauthorized modification or deletion of audit logs, remediation history, or vulnerability records to erase evidence or alter findings. - **Mitigation Strategy:** Store critical logs in WORM/append-only storage, forward copies to a hardened SIEM, apply strict IAM and separation of duties for log access, cryptographically sign logs, and enable integrity verification. **THR-019** - External Integrations - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed events or uses stolen integration credentials to manipulate the platform (trigger scans, inject malicious webhooks). - **Mitigation Strategy:** Enforce webhook signature verification, use short-lived integration credentials, require mutual TLS or IP allowlisting where possible, restrict delegated permissions, and monitor integration behavior for anomalies. **THR-020** - External Integrations - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Integration tokens, API keys, or credentials logged in plain-text (application logs, access logs, stack traces) or included in notifications (Slack/Teams) leading to compromise of third-party services. - **Mitigation Strategy:** Redact secrets from logs and notifications, store tokens encrypted (KMS), rotate integration tokens frequently, implement scoped tokens with minimal privileges, and apply strict logging policies to avoid sensitive data in outputs. **THR-022** - Repository Integration (OAuth) - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** OAuth application granted excessive scopes or abused consent to gain broad repository access; attacker leverages app to exfiltrate code or create/merge PRs. - **Mitigation Strategy:** Require least-privilege scopes for OAuth apps, implement granular scope review and just-in-time elevation for higher scopes, show clear consent screens, use fine-grained tokens, and implement periodic automatic token and scope reviews. **THR-023** - CI/CD Integrations - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Malicious pipeline step or compromised CI runner introduced via a PR allows execution of malicious code with elevated privileges (e.g., pushing to production or accessing secrets), especially when automatic merges are permitted. - **Mitigation Strategy:** Enforce pipeline protection (trusted runners), require CI job signatures or provenance, restrict secrets to ephemeral tokens injected only at runtime, require code reviews for pipeline changes, and avoid automatic merge-to-main without approvals. **THR-026** - Data Handling / Caching - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Cache poisoning or tenant key collisions cause cached analysis results, diffs, or findings to be returned to the wrong tenant or user, exposing proprietary code or vulnerability data. - **Mitigation Strategy:** Enforce tenant-scoped cache keys, encrypt cache values where sensitive, set conservative TTLs for cached code/artifacts, isolate caches per tenant when feasible, and validate cache hit tenant context before returning results. **THR-027** - Application Services / APIs - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Classic injection attacks (SQL injection, command injection) or unsafe deserialization in API endpoints exposing back-end data stores or enabling remote code execution within application services. - **Mitigation Strategy:** Use parameterized queries/ORMs, strict input validation and allow-lists, safe deserialization libraries, WAF protections, regular dependency vulnerability management, and secure coding practices plus SAST/DAST testing in CI. **THR-028** - Edge/API Gateway - **Category:** Elevation of Privilege - **Likelihood:** Low | **Impact:** High - **Risk Level:** High - **Description:** Misconfiguration in gateway RBAC or routing allows privilege escalation to administrative APIs (e.g., a normal user can access management endpoints or modify ACLs). - **Mitigation Strategy:** Harden gateway admin endpoints behind separate network controls and MFA, apply strict RBAC/ABAC for management APIs, separate admin plane from data plane, perform regular configuration audits, and use automated policy-as-code tests on deployments. ### Medium Risk Threats **THR-005** - Frontend Layer - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating PRs with suggested fixes) performed by authenticated users without their knowledge. - **Mitigation Strategy:** Enforce anti-CSRF tokens or same-site cookies for state-changing operations, require re-authentication/MFA for critical actions, validate Origin/Referer, and use strict CORS policies. **THR-007** - Application Services - **Category:** Repudiation - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who initiated a scan, who created/approved a PR), complicating incident response and compliance. - **Mitigation Strategy:** Generate immutable, tamper-evident audit logs with strong timestamps, forward logs to a hardened SIEM, implement write-once storage for critical logs, cryptographically sign important events, and implement log retention and access controls. **THR-013** - AI Analysis Engine / Vulnerability Feeds - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability metadata causing false positives/negatives or incorrect remediation advice. - **Mitigation Strategy:** Use signed/verifiable CVE feeds, cross-validate vulnerabilities from multiple trusted sources, implement caching with integrity verification, and maintain an internal vulnerability DB with human review for high-impact items. **THR-021** - External Integrations - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Flooding of webhook endpoints or continuous integration triggers to consume resources (e.g., force repeated scanning or push heavy workloads) causing degraded service. - **Mitigation Strategy:** Implement webhook rate limiting, signature verification, debounce/aggregation for repeated events, and require challenge-response during initial webhook registration. Provide per-tenant quotas. **THR-024** - Compliance and Reporting - **Category:** Repudiation - **Likelihood:** Low | **Impact:** Medium - **Risk Level:** Medium - **Description:** Tampering with compliance reports or trend analysis dashboards (intentional or accidental) to hide vulnerabilities, creating false compliance posture. - **Mitigation Strategy:** Cryptographically sign and timestamp exported reports, maintain immutable archived copies for audits, enforce access controls for report generation and modification, and monitor report generation and download events. **THR-025** - Team Collaboration - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Comment threads or dashboards inadvertently exposing sensitive code, PII, or secrets to unauthorized team members or external collaborators. - **Mitigation Strategy:** Enforce RBAC for comment visibility, auto-redact detected secrets and sensitive code snippets in comments, allow configurable visibility (private/public), and provide data minimization by default. **Total Threats:** 28 --- ## Appendix D: Complete Requirements Traceability Matrix This appendix provides complete end-to-end traceability from requirements through threats to controls and verification. ### Full Traceability Table | Req ID | Requirement | Category | Sensitivity | Threat IDs | Security Controls | Priority | Verification | Status | |--------|-------------|----------|-------------|------------|----------------|----------|--------------|--------| | REQ-001 | Repository integration with GitHub, GitLab, and Bi... | Repository Integration / Authentication | High | THR-002, THR-003, THR-005 +7 | [OWASP] ASVS 2.3.1, [NIST] AC-3, [ISO27001] A.9.4.2 | Critical | Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens., Inspect access control logic; run tests that attempt unauthorized repo access; review logs confirming enforcement of authorization decisions. | Pending | | REQ-002 | Support multi-language parsing and analysis for Py... | Analysis Engine | Medium | THR-009, THR-010, THR-011 +6 | [OWASP] ASVS 8.1.1, [NIST] SA-11, [OWASP] ASVS 14.1.1 | High | Review threat models and rulesets for each supported language; test scanners against seeded test cases covering language-specific issues., Audit sampled findings, review false-positive rates per language, and confirm manual review steps exist in process documentation. | Pending | | REQ-003 | Provide an LLM-based code review engine exposed vi... | Model Management / Inference | High | THR-001, THR-002, THR-003 +7 | [NIST] SA-15, [ISO27001] A.12.5.1, [OWASP] ASVS 12.1.1 | Critical | Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers., Review model versioning logs, CI/CD pipelines for model deployment, and security gating on model promotion to production. | Pending | | REQ-004 | Perform static code analysis (SAST) and pattern re... | Analysis Engine / Security Testing | High | THR-004, THR-005, THR-006 +7 | [OWASP] ASVS 14.2.1, [NIST] RA-5 | Critical | Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates., Verify scheduled scans run; check vulnerability tickets and remediation timelines; validate coverage of scanning tools. | Pending | | REQ-005 | Dependency scanning that identifies libraries, ver... | Dependency Management / Vulnerability Management | High | THR-003, THR-005, THR-008 +6 | [NIST] SA-12, [OWASP] ASVS 14.4.1, [ISO27001] A.15.1.1 | Critical | Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI., Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans. | Pending | | REQ-006 | Secret detection across code, config files, and co... | Secret Management / Data Protection | High | THR-003, THR-005, THR-006 +7 | [NIST] SA-12, [OWASP] ASVS 14.4.1, [ISO27001] A.15.1.1 | Critical | Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI., Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans. | Pending | | REQ-007 | Generate AI remediation suggestions with code snip... | Remediation / Developer Productivity | Medium | THR-004, THR-006, THR-008 +7 | [NIST] PL-4, [OWASP] ASVS 12.1.1, [ISO27001] A.14.2.3 | High | Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions., Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage. | Pending | | REQ-008 | Support automatic pull request creation with propo... | Automation / CI/CD Integration | High | THR-002, THR-003, THR-005 +4 | [NIST] CM-3, [OWASP] ASVS 14.1.1, [ISO27001] A.12.1.2 | Critical | Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs., Verify workflow enforces approvals, review automated PRs created and ensure a rollback path exists with audit logs. | Pending | | REQ-009 | Provide severity scoring and risk assessment that ... | Risk Management | Medium | THR-004, THR-009, THR-010 +7 | [NIST] RA-3, [OWASP] ASVS 14.4.1, [ISO27001] A.8.2.1 | Critical | Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans., Check classification registers and verify scoring integrates classification attributes into prioritization. | Pending | | REQ-010 | Context-aware explanations for each finding, inclu... | Reporting / Developer Support | Medium | THR-004, THR-009, THR-010 +7 | [NIST] PL-4, [OWASP] ASVS 12.1.1, [ISO27001] A.14.2.3 | High | Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions., Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage. | Pending | | REQ-011 | Developer dashboards displaying personalized secur... | Team Collaboration / Analytics | Medium | THR-003, THR-009, THR-010 +6 | [OWASP] ASVS 12.1.1, [NIST] AU-6, [ISO27001] A.7.2.2 | High | Test API endpoints for access control bypass, review role mappings, and inspect UI rendering for sensitive data leaks., Verify links to training are available and that metrics are used in awareness programs. | Pending | | REQ-012 | Support comment threads on identified issues, assi... | Collaboration / Workflow | Medium | THR-001, THR-004, THR-011 +7 | None | Medium | Manual Review | Pending | | REQ-013 | Integrate with CI/CD pipelines (e.g., GitHub Actio... | CI/CD Integration | High | THR-001, THR-002, THR-004 +7 | [OWASP] ASVS 14.1.1, [NIST] SA-11, [ISO27001] A.14.2.1 | Critical | Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement., Review CI pipeline configs, confirm scans run on pre-merge and post-merge events, and validate PR blocking behavior for critical issues. | Pending | | REQ-014 | Send configurable notifications for critical findi... | Notifications / Collaboration | Medium | THR-004, THR-010, THR-011 +5 | None | Medium | Manual Review | Pending | | REQ-015 | Provide OWASP Top 10 checks and allow creation/enf... | Compliance / Policy Enforcement | Medium | THR-001, THR-009, THR-016 +4 | [OWASP] ASVS 14.4.1, [NIST] PL-2, [ISO27001] A.5.1.1 | Critical | Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated., Test custom rule creation, verify policy mapping to OWASP categories, and confirm enforcement in scans and gates. | Pending | | REQ-016 | Maintain immutable audit logs of all analyses, fin... | Audit / Compliance | High | THR-001, THR-002, THR-003 +7 | [NIST] AU-2, [OWASP] ASVS 12.3.1, [NIST] AU-9 | Critical | Review audit event definitions, sample logs for required events, and validate retention and integrity protections., Verify cryptographic log integrity, attempt tamper tests in controlled env, and review log access controls. | Pending | | REQ-017 | Exportable compliance reports and evidence package... | Reporting / Compliance | Medium | THR-007, THR-008, THR-010 +7 | [ISO27001] A.18.2.3, [NIST] CA-7, [OWASP] ASVS 12.1.1 | High | Test export APIs for access control enforcement and verify signatures on exported packages., Validate that continuous monitoring feeds into report generation and that exported reports contain verifiable signatures. | Pending | | REQ-018 | Model management features including model versioni... | Model Management / Governance | High | THR-009, THR-010, THR-012 +7 | None | Medium | Manual Review | Pending | | REQ-019 | Enable custom model training on customers' codebas... | Model Training / Data Privacy | High | THR-002, THR-005, THR-007 +7 | [NIST] PL-4, [ISO27001] A.8.2.3, [OWASP] ASVS 14.3.1 | Critical | Inspect rules of behavior, check anonymization pipelines, and confirm consent records for training datasets., Run secret scans on training datasets, validate redaction effectiveness, and review provenance logs. | Pending | | REQ-020 | Model performance monitoring and drift detection w... | Model Monitoring / Quality | Medium | THR-009, THR-010, THR-012 +7 | [NIST] SA-15, [OWASP] ASVS 12.1.1, [NIST] SI-4 | Critical | Review governance artifacts, model registries, A/B testing configs, and access controls for model artifacts., Verify authN/Z on endpoints, confirm version headers are present in logs, and review telemetry ingestion. | Pending | | REQ-021 | Secure processing and storage of code snippets, an... | Data Handling / Security | High | THR-001, THR-002, THR-003 +7 | [NIST] SC-13, [ISO27001] A.10.1.1, [OWASP] ASVS 2.4.1 | Critical | Inspect encryption settings, redaction logic, and confirm secrets are stored only in approved vaults with access auditing., Review cryptography policy and sample stored artifacts for correct encryption and redaction settings. | Pending | | REQ-022 | Maintain and update a vulnerability database mappe... | Vulnerability Management / Threat Intelligence | Medium | THR-008, THR-013, THR-014 +4 | [NIST] CM-8, [OWASP] ASVS 14.4.1, [ISO27001] A.12.6.1 | Critical | Examine vulnerability management reports, SLA adherence, and evidence of action on identified CVEs., Review feed ingestion logs, confirm CVE-to-component mappings, and check remediation ticket lifecycle. | Pending | | REQ-023 | Cache analysis results for performance with secure... | Performance / Data Management | Medium | THR-001, THR-004, THR-005 +7 | [OWASP] ASVS 14.3.1, [NIST] SC-12, [ISO27001] A.10.1.1 | Critical | Inspect KM systems, verify rotation and revocation procedures were executed for exposed secrets, and review access controls to secrets., Run secret scanning on historical repositories, validate detection of seeded secrets, and confirm blocking policy enforcement. | Pending | | REQ-024 | Implement fine-grained access control and RBAC for... | Authentication / Authorization | High | THR-001, THR-002, THR-003 +7 | [NIST] AC-2, [OWASP] ASVS 2.2.1, [ISO27001] A.9.1.2 | Critical | Test role-based access enforcement for UI and APIs and verify session handling behaviors., Review account provisioning logs, role assignments and deprovision records; perform access reviews. | Pending | | REQ-025 | API gateway protections: API key management, OAuth... | API Security / Operational Security | High | THR-001, THR-002, THR-003 +7 | [OWASP] ASVS 12.1.1, [NIST] IA-5, [ISO27001] A.13.1.1 | Critical | Review authenticator policies, test brute-force protections and check account lockout logs., Review edge proxy configs, WAF rules, and DDoS protection telemetry during simulated abuse. | Pending | | REQ-026 | Support deployment options: SaaS with VPC peering,... | Deployment / Infrastructure | High | THR-008, THR-009, THR-010 +7 | [OWASP] ASVS 14.1.1, [NIST] SC-7, [ISO27001] A.8.1.3 | High | Review deployment manifests for isolation features and validate data residency enforcement in sample deployments., Pen-test network boundaries, inspect VPC configs, and verify management plane access restrictions. | Pending | | REQ-027 | Data usage and consent controls including the abil... | Privacy / Data Governance | High | THR-003, THR-004, THR-008 +7 | [ISO27001] A.18.1.4, [NIST] PL-4, [OWASP] ASVS 2.4.1 | Critical | Check rules of behavior documentation and verify system honors stored user preferences during training runs., Inspect dataset tagging and training pipelines, confirm that opt-out tags prevent inclusion in training datasets. | Pending | | REQ-028 | Integrate platform monitoring with SIEM/SOAR for s... | Monitoring / Incident Response | High | THR-007, THR-009, THR-014 +1 | [NIST] SI-4, [ISO27001] A.16.1.5, [OWASP] ASVS 12.3.1 | Critical | Review incident response plans, confirm SOPs for SIEM alerts, and evaluate results of tabletop or live exercises., Audit logs for required context fields, verify SIEM correlation rules and test full incident response flow. | Pending | **Total Requirements Tracked:** 28 ### Detailed Requirement Mappings The following section provides detailed traceability for each requirement: #### REQ-001: Repository integration with GitHub, GitLab, and Bitbucket using OAuth-based authentication, webhook ... **Related Threats:** - **THR-002**: Manipulation of inbound API requests or webhook payloads en route to backend (e.... - **THR-003**: API gateway or edge infrastructure overwhelmed by high request volume or crafted... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - **THR-006**: Forged repository webhook events or API calls cause the application to perform u... - **THR-008**: Broken access control leading to unauthorized users (or tenants) reading reposit... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 2.3.1**: [OWASP] Ensure secure single sign-on, federated authentication and OAuth integration fol... - **[NIST] AC-3**: [NIST] The information system enforces approved authorizations for controlling access t... - **[ISO27001] A.9.4.2**: [ISO27001] Where required by the access control policy, secure log-on procedures shall be i... **Verification:** Review authentication configuration, MFA enforcement for accounts with repo access, and verify secure handling of OAuth redirect URIs and tokens., Inspect access control logic; run tests that attempt unauthorized repo access; review logs confirming enforcement of authorization decisions., Review OAuth implementation code/config; inspect scope requested during OAuth flows; verify webhook signature verification and token storage protections during pen test and code review. **Priority:** Critical | **Status:** Pending --- #### REQ-002: Support multi-language parsing and analysis for Python, Java, JavaScript/TypeScript, Go, and an exte... **Related Threats:** - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - **THR-012**: Excessive inference requests (automated or malicious) saturate model hosting or ... - **THR-013**: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability ... - *...and 4 more threats* **Security Controls:** - **[OWASP] ASVS 8.1.1**: [OWASP] Perform architecture and design level analysis and include language-specific sec... - **[NIST] SA-11**: [NIST] The organization requires developers to employ security testing tools and techni... - **[OWASP] ASVS 14.1.1**: [OWASP] Source code should be reviewed by automated tools and manually to detect flaws a... **Verification:** Review threat models and rulesets for each supported language; test scanners against seeded test cases covering language-specific issues., Audit sampled findings, review false-positive rates per language, and confirm manual review steps exist in process documentation., Verify tool inventory and configurations include language-specific scanners; run comparative analysis with benchmark projects. **Priority:** High | **Status:** Pending --- #### REQ-003: Provide an LLM-based code review engine exposed via secure API endpoints for inference, supporting f... **Related Threats:** - **THR-001**: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi... - **THR-002**: Manipulation of inbound API requests or webhook payloads en route to backend (e.... - **THR-003**: API gateway or edge infrastructure overwhelmed by high request volume or crafted... - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - *...and 5 more threats* **Security Controls:** - **[NIST] SA-15**: [NIST] The organization requires that development processes and tools support secure de... - **[ISO27001] A.12.5.1**: [ISO27001] Changes to organization assets and services should be controlled and managed inc... - **[OWASP] ASVS 12.1.1**: [OWASP] Ensure secure design and versioning for APIs and that API endpoints enforce auth... **Verification:** Pen-test API endpoints, review auth flows, check logs for model inference calls and version identifiers., Review model versioning logs, CI/CD pipelines for model deployment, and security gating on model promotion to production., Inspect change tickets for model deployments, verify rollback procedures, and confirm version metadata is immutable and auditable. **Priority:** Critical | **Status:** Pending --- #### REQ-004: Perform static code analysis (SAST) and pattern recognition to detect insecure coding patterns, misc... **Related Threats:** - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - **THR-006**: Forged repository webhook events or API calls cause the application to perform u... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 14.2.1**: [OWASP] Use automated static analysis tools to detect common security weaknesses and ant... - **[NIST] RA-5**: [NIST] The organization scans for vulnerabilities and identifies vulnerabilities in sys... **Verification:** Review static analysis tool outputs on representative codebases; test seeded vulnerabilities and measure detection rates., Verify scheduled scans run; check vulnerability tickets and remediation timelines; validate coverage of scanning tools. **Priority:** Critical | **Status:** Pending --- #### REQ-005: Dependency scanning that identifies libraries, versions, and maps to known CVEs (NVD/CVE feeds), wit... **Related Threats:** - **THR-003**: API gateway or edge infrastructure overwhelmed by high request volume or crafted... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - **THR-008**: Broken access control leading to unauthorized users (or tenants) reading reposit... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - *...and 4 more threats* **Security Controls:** - **[NIST] SA-12**: [NIST] The organization manages supply chain risks and implements protections against s... - **[OWASP] ASVS 14.4.1**: [OWASP] Verify and manage third-party components and dependencies; ensure known vulnerab... - **[ISO27001] A.15.1.1**: [ISO27001] Organizations should identify and manage security risks associated with supplier... **Verification:** Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI., Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans., Audit dependency scan reports, confirm mapping to CVE/NVD, and verify remediation tickets exist for critical findings. **Priority:** Critical | **Status:** Pending --- #### REQ-006: Secret detection across code, config files, and commits using pattern detection, entropy checks, and... **Related Threats:** - **THR-003**: API gateway or edge infrastructure overwhelmed by high request volume or crafted... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - **THR-006**: Forged repository webhook events or API calls cause the application to perform u... - **THR-008**: Broken access control leading to unauthorized users (or tenants) reading reposit... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - *...and 5 more threats* **Security Controls:** - **[NIST] SA-12**: [NIST] The organization manages supply chain risks and implements protections against s... - **[OWASP] ASVS 14.4.1**: [OWASP] Verify and manage third-party components and dependencies; ensure known vulnerab... - **[ISO27001] A.15.1.1**: [ISO27001] Organizations should identify and manage security risks associated with supplier... **Verification:** Review SBOMs, scan logs mapping components to CVEs, and test vulnerability gating in CI., Review supplier agreements, check that supply-chain scan results are tied to supplier records and remediation plans., Audit dependency scan reports, confirm mapping to CVE/NVD, and verify remediation tickets exist for critical findings. **Priority:** Critical | **Status:** Pending --- #### REQ-007: Generate AI remediation suggestions with code snippets, unit/regression test suggestions, and contex... **Related Threats:** - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-006**: Forged repository webhook events or API calls cause the application to perform u... - **THR-008**: Broken access control leading to unauthorized users (or tenants) reading reposit... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - *...and 5 more threats* **Security Controls:** - **[NIST] PL-4**: [NIST] Define acceptable use and rules for automated tools and AI, specifying responsib... - **[OWASP] ASVS 12.1.1**: [OWASP] Ensure APIs delivering code suggestions enforce authentication, authorization, a... - **[ISO27001] A.14.2.3**: [ISO27001] Protection of data used for testing and development including sanitized code sam... **Verification:** Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions., Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage., Pen-test suggestion APIs, inspect redaction logic for outputs, and verify auth and logging configuration. **Priority:** High | **Status:** Pending --- #### REQ-008: Support automatic pull request creation with proposed fixes, configurable auto-apply actions, and an... **Related Threats:** - **THR-002**: Manipulation of inbound API requests or webhook payloads en route to backend (e.... - **THR-003**: API gateway or edge infrastructure overwhelmed by high request volume or crafted... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - **THR-006**: Forged repository webhook events or API calls cause the application to perform u... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - *...and 2 more threats* **Security Controls:** - **[NIST] CM-3**: [NIST] Changes to system configuration and software must be controlled, including appro... - **[OWASP] ASVS 14.1.1**: [OWASP] Source code changes should be subject to review and must be tracked by secure pr... - **[ISO27001] A.12.1.2**: [ISO27001] Changes to information processing facilities and systems should be managed follo... **Verification:** Inspect PR workflows, confirm human approval requirements, and validate traceability between findings and PRs., Verify workflow enforces approvals, review automated PRs created and ensure a rollback path exists with audit logs., Review change records for auto-applied fixes, confirm approval traces, and test rollback processes. **Priority:** Critical | **Status:** Pending --- #### REQ-009: Provide severity scoring and risk assessment that combines CVSS, exploitability, code context (usage... **Related Threats:** - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - **THR-016**: Leakage of model binaries or customer-specific training data/artifacts used for ... - *...and 5 more threats* **Security Controls:** - **[NIST] RA-3**: [NIST] Conduct risk assessments to identify threats and vulnerabilities and determine r... - **[OWASP] ASVS 14.4.1**: [OWASP] Vulnerabilities should be prioritized by severity and context to guide remediati... - **[ISO27001] A.8.2.1**: [ISO27001] Information should be classified to support risk-based handling and prioritizati... **Verification:** Audit prioritization rules, check that critical items are consistently prioritized and tied to remediation plans., Check classification registers and verify scoring integrates classification attributes into prioritization., Review risk assessment methodology, sample scored findings and ensure scoring matches defined criteria and business context. **Priority:** Critical | **Status:** Pending --- #### REQ-010: Context-aware explanations for each finding, including code locations, stack traces, reproduction st... **Related Threats:** - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - **THR-016**: Leakage of model binaries or customer-specific training data/artifacts used for ... - *...and 5 more threats* **Security Controls:** - **[NIST] PL-4**: [NIST] Define acceptable use and rules for automated tools and AI, specifying responsib... - **[OWASP] ASVS 12.1.1**: [OWASP] Ensure APIs delivering code suggestions enforce authentication, authorization, a... - **[ISO27001] A.14.2.3**: [ISO27001] Protection of data used for testing and development including sanitized code sam... **Verification:** Review rules of behavior documentation and check that human-in-the-loop approvals exist for critical suggestions., Review data sanitation logs, run tests with sensitive inputs to ensure redaction, and audit stored generated snippets for leakage., Pen-test suggestion APIs, inspect redaction logic for outputs, and verify auth and logging configuration. **Priority:** High | **Status:** Pending --- #### REQ-011: Developer dashboards displaying personalized security metrics, team-level metrics, live issue queues... **Related Threats:** - **THR-003**: API gateway or edge infrastructure overwhelmed by high request volume or crafted... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - **THR-012**: Excessive inference requests (automated or malicious) saturate model hosting or ... - *...and 4 more threats* **Security Controls:** - **[OWASP] ASVS 12.1.1**: [OWASP] APIs providing dashboards must enforce authentication, authorization, and secure... - **[NIST] AU-6**: [NIST] Review and analyze audit records and report findings to support security monitor... - **[ISO27001] A.7.2.2**: [ISO27001] Employees should receive appropriate awareness and training to support informati... **Verification:** Test API endpoints for access control bypass, review role mappings, and inspect UI rendering for sensitive data leaks., Verify links to training are available and that metrics are used in awareness programs., Compare dashboard metrics against raw audit logs to ensure accuracy and completeness. **Priority:** High | **Status:** Pending --- #### REQ-012: Support comment threads on identified issues, assignment workflows, and integrations that allow deve... **Related Threats:** - **THR-001**: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi... - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - **THR-018**: Unauthorized modification or deletion of audit logs, remediation history, or vul... - **THR-019**: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed ... - *...and 5 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- #### REQ-013: Integrate with CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins) to run pre-merge and post-... **Related Threats:** - **THR-001**: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi... - **THR-002**: Manipulation of inbound API requests or webhook payloads en route to backend (e.... - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - **THR-007**: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 14.1.1**: [OWASP] Incorporate automated security testing into the development lifecycle and CI/CD ... - **[NIST] SA-11**: [NIST] Require developer security testing and evaluation, including integration into bu... - **[ISO27001] A.14.2.1**: [ISO27001] Establish secure development policies including integration of security testing ... **Verification:** Confirm security tools are executed in pipelines, review gating rules, and examine historical pipeline runs for enforcement., Review CI pipeline configs, confirm scans run on pre-merge and post-merge events, and validate PR blocking behavior for critical issues., Review secure development policy and pipeline configurations for evidence of enforcement. **Priority:** Critical | **Status:** Pending --- #### REQ-014: Send configurable notifications for critical findings to Slack and Microsoft Teams with secure links... **Related Threats:** - **THR-004**: Cross-site scripting (XSS) or unsafe rendering in the SPA leaks code snippets, f... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - **THR-018**: Unauthorized modification or deletion of audit logs, remediation history, or vul... - **THR-019**: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed ... - *...and 3 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- #### REQ-015: Provide OWASP Top 10 checks and allow creation/enforcement of custom security policies and rule sets... **Related Threats:** - **THR-001**: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-016**: Leakage of model binaries or customer-specific training data/artifacts used for ... - **THR-019**: Compromised third-party system (e.g., CI/CD provider or Git host) sends spoofed ... - **THR-023**: Malicious pipeline step or compromised CI runner introduced via a PR allows exec... - *...and 2 more threats* **Security Controls:** - **[OWASP] ASVS 14.4.1**: [OWASP] Enforce security policies including OWASP Top 10 and provide mechanisms for cust... - **[NIST] PL-2**: [NIST] Develop and disseminate system and communications protection policies and proced... - **[ISO27001] A.5.1.1**: [ISO27001] Management should define a set of information security policies to provide manag... **Verification:** Review policy documentation and evidence of dissemination; test that procedures are followed when rules are updated., Test custom rule creation, verify policy mapping to OWASP categories, and confirm enforcement in scans and gates., Check for signed policy documents and traceability between policies and enforcement mechanisms. **Priority:** Critical | **Status:** Pending --- #### REQ-016: Maintain immutable audit logs of all analyses, findings, remediation actions, approvals, and access ... **Related Threats:** - **THR-001**: Compromise or forging of OAuth tokens/JWTs (stolen tokens, replayed tokens, unsi... - **THR-002**: Manipulation of inbound API requests or webhook payloads en route to backend (e.... - **THR-003**: API gateway or edge infrastructure overwhelmed by high request volume or crafted... - **THR-007**: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who... - **THR-008**: Broken access control leading to unauthorized users (or tenants) reading reposit... - *...and 5 more threats* **Security Controls:** - **[NIST] AU-2**: [NIST] The information system provides audit records for the organization-defined event... - **[OWASP] ASVS 12.3.1**: [OWASP] Ensure that security-relevant events are logged with integrity and protected fro... - **[NIST] AU-9**: [NIST] Protect audit records from unauthorized access, modification, and deletion to pr... **Verification:** Review audit event definitions, sample logs for required events, and validate retention and integrity protections., Verify cryptographic log integrity, attempt tamper tests in controlled env, and review log access controls., Review access lists for log storage, check encryption keys usage, and validate log retention policies. **Priority:** Critical | **Status:** Pending --- #### REQ-017: Exportable compliance reports and evidence packages (PDF/CSV/JSON) covering findings, remediation hi... **Related Threats:** - **THR-007**: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who... - **THR-008**: Broken access control leading to unauthorized users (or tenants) reading reposit... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-011**: LLM-generated remediation automatically introduces code that elevates privileges... - **THR-013**: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability ... - *...and 5 more threats* **Security Controls:** - **[ISO27001] A.18.2.3**: [ISO27001] Information security should be reviewed regularly and results available to demon... - **[NIST] CA-7**: [NIST] Establish continuous monitoring processes to produce evidence and reports of sys... - **[OWASP] ASVS 12.1.1**: [OWASP] APIs that export reports must enforce access controls and ensure integrity of ex... **Verification:** Test export APIs for access control enforcement and verify signatures on exported packages., Validate that continuous monitoring feeds into report generation and that exported reports contain verifiable signatures., Produce sample evidence packages for audits and confirm completeness against compliance checklists. **Priority:** High | **Status:** Pending --- #### REQ-018: Model management features including model versioning, A/B testing frameworks, staged rollouts, and l... **Related Threats:** - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-012**: Excessive inference requests (automated or malicious) saturate model hosting or ... - **THR-014**: Secrets (API keys, tokens, private keys) stored in persistent storage, caches, b... - **THR-015**: Unauthorized modification or replacement of model binaries or metadata (model po... - *...and 5 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- #### REQ-019: Enable custom model training on customers' codebase with controls: opt-in consent, isolated training... **Related Threats:** - **THR-002**: Manipulation of inbound API requests or webhook payloads en route to backend (e.... - **THR-005**: Cross-Site Request Forgery (CSRF) causing unauthorized actions (e.g., creating P... - **THR-007**: Insufficient or tamperable audit logs enabling actors to deny actions (e.g., who... - **THR-008**: Broken access control leading to unauthorized users (or tenants) reading reposit... - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - *...and 5 more threats* **Security Controls:** - **[NIST] PL-4**: [NIST] Define acceptable use and rules for systems handling sensitive data including re... - **[ISO27001] A.8.2.3**: [ISO27001] Ensure appropriate handling of assets including data used for training, protecti... - **[OWASP] ASVS 14.3.1**: [OWASP] Protect sensitive data used for model training and avoid inclusion of secrets in... **Verification:** Inspect rules of behavior, check anonymization pipelines, and confirm consent records for training datasets., Run secret scans on training datasets, validate redaction effectiveness, and review provenance logs., Audit access to training datasets and review applied privacy-preserving measures and their tuning parameters. **Priority:** Critical | **Status:** Pending --- #### REQ-020: Model performance monitoring and drift detection with alerts, metrics (precision/recall/false positi... **Related Threats:** - **THR-009**: Sensitive source code or secrets are transmitted to or stored on external manage... - **THR-010**: Poisoning of training data or prompt injection via crafted code causing models t... - **THR-012**: Excessive inference requests (automated or malicious) saturate model hosting or ... - **THR-013**: Poisoned or tampered CVE/NVD feeds deliver incorrect or malicious vulnerability ... - **THR-014**: Secrets (API keys, tokens, private keys) stored in persistent storage, caches, b... - *...and 5 more threats* **Security Controls:** - **[NIST] SA-15**: [NIST] The organization ensures development processes and tools support secure design, ... - **[OWASP] ASVS 12.1.1**: [OWASP] Ensure API endpoints enforce authentication, authorization, and logging to suppo... - **[NIST] SI-4**: [NIST] Monitor the information system to detect attacks and potential misuse, including... **Verification:** Review governance artifacts, model registries, A/B testing configs, and access controls for model artifacts., Verify authN/Z on endpoints, confirm version headers are present in logs, and review telemetry ingestion., Review monitoring rules, simulate drift scenarios, and confirm alerts trigger defined responses. **Priority:** Critical | **Status:** Pending --- *Showing detailed mappings for 20 of 28 requirements.* --- ## Appendix E: References - [OWASP ASVS 5.0](https://owasp.org/www-project-application-security-verification-standard/) - [NIST Cybersecurity Framework](https://www.nist.gov/cyberframework) - [ISO/IEC 27001:2022](https://www.iso.org/standard/27001) - [OWASP Top 10](https://owasp.org/www-project-top-ten/) - [MITRE ATT&CK Framework](https://attack.mitre.org/) --- *End of Report - Generated by Security Requirements Analysis System v2.0* *Generated: 2025-11-19 19:04:00*